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structure learning using forced pruning

markov networks are widely used in many machine learning applications including natural language processing, computer vision, and bioinformatics . learning markov networks have many complications ranging from intractable computations involved to the possibility of learning a model with a huge number of parameters. in this report, we provide a computationally tractable greedy heuristic for learning markov networks structure. the proposed heuristic results in a model with a limited predefined number of parameters. we ran our method on 3 fully-observed real datasets, and we observed that our method is doing comparably good to the state of the art methods.

deep reinforcement learning for intelligent transportation systems

intelligent transportation systems (itss) are envisioned to play a critical role in improving traffic flow and reducing congestion, which is a pervasive issue impacting urban areas around the globe. rapidly advancing vehicular communication and edge cloud computation technologies provide key enablers for smart traffic management. however, operating viable real-time actuation mechanisms on a practically relevant scale involves formidable challenges, e.g., policy iteration and conventional reinforcement learning (rl) techniques suffer from poor scalability due to state space explosion. motivated by these issues, we explore the potential for deep q-networks (dqn) to optimize traffic light control policies. as an initial benchmark, we establish that the dqn algorithms yield the "thresholding" policy in a single-intersection. next, we examine the scalability properties of dqn algorithms and their performance in a linear network topology with several intersections along a main artery. we demonstrate that dqn algorithms produce intelligent behavior, such as the emergence of "greenwave" patterns, reflecting their ability to learn favorable traffic light actuations.

bach2bach: generating music using a deep reinforcement learning approach

a model of music needs to have the ability to recall past details and have a clear, coherent understanding of musical structure. detailed in the paper is a deep reinforcement learning architecture that predicts and generates polyphonic music aligned with musical rules. the probabilistic model presented is a bi-axial lstm trained with a pseudo-kernel reminiscent of a convolutional kernel. to encourage exploration and impose greater global coherence on the generated music, a deep reinforcement learning approach dqn is adopted. when analyzed quantitatively and qualitatively, this approach performs well in composing polyphonic music.

a hybrid instance-based transfer learning method

in recent years, supervised machine learning models have demonstrated tremendous success in a variety of application domains. despite the promising results, these successful models are data hungry and their performance relies heavily on the size of training data. however, in many healthcare applications it is difficult to collect sufficiently large training datasets. transfer learning can help overcome this issue by transferring the knowledge from readily available datasets (source) to a new dataset (target). in this work, we propose a hybrid instance-based transfer learning method that outperforms a set of baselines including state-of-the-art instance-based transfer learning approaches. our method uses a probabilistic weighting strategy to fuse information from the source domain to the model learned in the target domain. our method is generic, applicable to multiple source domains, and robust with respect to negative transfer. we demonstrate the effectiveness of our approach through extensive experiments for two different applications.

distilling information from a flood: a possibility for the use of meta-analysis and systematic review in machine learning research

the current flood of information in all areas of machine learning research, from computer vision to reinforcement learning, has made it difficult to make aggregate scientific inferences. it can be challenging to distill a myriad of similar papers into a set of useful principles, to determine which new methodologies to use for a particular application, and to be confident that one has compared against all relevant related work when developing new ideas. however, such a rapidly growing body of research literature is a problem that other fields have already faced - in particular, medicine and epidemiology. in those fields, systematic reviews and meta-analyses have been used exactly for dealing with these issues and it is not uncommon for entire journals to be dedicated to such analyses. here, we suggest the field of machine learning might similarly benefit from meta-analysis and systematic review, and we encourage further discussion and development along this direction.

machine learning for yield curve feature extraction: application to illiquid corporate bonds

this paper studies an application of machine learning in extracting features from the historical market implied corporate bond yields. we consider an example of a hypothetical illiquid fixed income market. after choosing a surrogate liquid market, we apply the denoising autoencoder (dae) algorithm to learn the features of the missing yield parameters from the historical data of the instruments traded in the chosen liquid market. the dae algorithm is then challenged by two "point-in-time" inpainting algorithms taken from the image processing and computer vision domain. it is observed that, when tested on unobserved rate surfaces, the dae algorithm exhibits superior performance thanks to the features it has learned from the historical shapes of yield curves.

correspondence analysis of government expenditure patterns

we analyze expenditure patterns of discretionary funds by brazilian congress members. this analysis is based on a large dataset containing over $7$ million expenses made publicly available by the brazilian government. this dataset has, up to now, remained widely untouched by machine learning methods. our main contributions are two-fold: (i) we provide a novel dataset benchmark for machine learning-based efforts for government transparency to the broader research community, and (ii) introduce a neural network-based approach for analyzing and visualizing outlying expense patterns. our hope is that the approach presented here can inspire new machine learning methodologies for government transparency applicable to other developing nations.

exploring galaxy evolution with generative models

context. generative models open up the possibility to interrogate scientific data in a more data-driven way. aims: we propose a method that uses generative models to explore hypotheses in astrophysics and other areas. we use a neural network to show how we can independently manipulate physical attributes by encoding objects in latent space. methods: by learning a latent space representation of the data, we can use this network to forward model and explore hypotheses in a data-driven way. we train a neural network to generate artificial data to test hypotheses for the underlying physical processes. results: we demonstrate this process using a well-studied process in astrophysics, the quenching of star formation in galaxies as they move from low-to high-density environments. this approach can help explore astrophysical and other phenomena in a way that is different from current methods based on simulations and observations.

sequential experiment design for hypothesis verification

hypothesis testing is an important problem with applications in target localization, clinical trials etc. many active hypothesis testing strategies operate in two phases: an exploration phase and a verification phase. in the exploration phase, selection of experiments is such that a moderate level of confidence on the true hypothesis is achieved. subsequent experiment design aims at improving the confidence level on this hypothesis to the desired level. in this paper, the focus is on the verification phase. a confidence measure is defined and active hypothesis testing is formulated as a confidence maximization problem in an infinite-horizon average-reward partially observable markov decision process (pomdp) setting. the problem of maximizing confidence conditioned on a particular hypothesis is referred to as the hypothesis verification problem. the relationship between hypothesis testing and verification problems is established. the verification problem can be formulated as a markov decision process (mdp). optimal solutions for the verification mdp are characterized and a simple heuristic adaptive strategy for verification is proposed based on a zero-sum game interpretation of kullback-leibler divergences. it is demonstrated through numerical experiments that the heuristic performs better in some scenarios compared to existing methods in literature.

test of covariance and correlation matrices

based on a generalized cosine measure between two symmetric matrices, we propose a general framework for one-sample and two-sample tests of covariance and correlation matrices. we also develop a set of associated permutation algorithms for some common one-sample tests, such as the tests of sphericity, identity and compound symmetry, and the $k$-sample tests of multivariate equality of covariance or correlation matrices. the proposed method is very flexible in the sense that it does not assume any underlying distributions and data generation models. moreover, it allows data to have different marginal distributions in both the one-sample identity and $k$-sample tests. through real datasets and extensive simulations, we demonstrate that the proposed method performs well in terms of empirical type i error and power in a variety of hypothesis testing situations in which data of different sizes and dimensions are generated using different distributions and generation models.

parallel-tempered stochastic gradient hamiltonian monte carlo for approximate multimodal posterior sampling

we propose a new sampler that integrates the protocol of parallel tempering with the nos\'e-hoover (nh) dynamics. the proposed method can efficiently draw representative samples from complex posterior distributions with multiple isolated modes in the presence of noise arising from stochastic gradient. it potentially facilitates deep bayesian learning on large datasets where complex multimodal posteriors and mini-batch gradient are encountered.

reducing seed bias in respondent-driven sampling by estimating block transition probabilities

respondent-driven sampling (rds) is a popular approach to study marginalized or hard-to-reach populations. it collects samples from a networked population by incentivizing participants to refer their friends into the study. one major challenge in analyzing rds samples is seed bias. seed bias refers to the fact that when the social network is divided into multiple communities (or blocks), the rds sample might not provide a balanced representation of the different communities in the population, and such unbalance is correlated with the initial participant (or the seed). in this case, the distributions of estimators are typically non-trivial mixtures, which are determined (1) by the seed and (2) by how the referrals transition from one block to another. this paper shows that (1) block-transition probabilities are easy to estimate with high accuracy, and (2) we can use these estimated block-transition probabilities to estimate the stationary distribution over blocks and thus, an estimate of the block proportions. this stationary distribution on blocks has previously been used in the rds literature to evaluate whether the sampling process has appeared to `mix'. we use these estimated block proportions in a simple post-stratified (ps) estimator that greatly diminishes seed bias. by aggregating over the blocks/strata in this way, we prove that the ps estimator is $\sqrt{n}$-consistent under a markov model, even when other estimators are not. simulations show that the ps estimator has smaller root mean square error (rmse) compared to the state-of-the-art estimators.

eenmf: an end-to-end neural matching framework for e-commerce sponsored search

e-commerce sponsored search contributes an important part of revenue for the e-commerce company. in consideration of effectiveness and efficiency, a large-scale sponsored search system commonly adopts a multi-stage architecture. we name these stages as ad retrieval, ad pre-ranking and ad ranking. ad retrieval and ad pre-ranking are collectively referred to as ad matching in this paper. we propose an end-to-end neural matching framework (eenmf) to model two tasks---vector-based ad retrieval and neural networks based ad pre-ranking. under the deep matching framework, vector-based ad retrieval harnesses user recent behavior sequence to retrieve relevant ad candidates without the constraint of keyword bidding. simultaneously, the deep model is employed to perform the global pre-ranking of ad candidates from multiple retrieval paths effectively and efficiently. besides, the proposed model tries to optimize the pointwise cross-entropy loss which is consistent with the objective of predict models in the ranking stage. we conduct extensive evaluation to validate the performance of the proposed framework. in the real traffic of a large-scale e-commerce sponsored search, the proposed approach significantly outperforms the baseline.

a retrieve-and-edit framework for predicting structured outputs

for the task of generating complex outputs such as source code, editing existing outputs can be easier than generating complex outputs from scratch. with this motivation, we propose an approach that first retrieves a training example based on the input (e.g., natural language description) and then edits it to the desired output (e.g., code). our contribution is a computationally efficient method for learning a retrieval model that embeds the input in a task-dependent way without relying on a hand-crafted metric or incurring the expense of jointly training the retriever with the editor. our retrieve-and-edit framework can be applied on top of any base model. we show that on a new autocomplete task for github python code and the hearthstone cards benchmark, retrieve-and-edit significantly boosts the performance of a vanilla sequence-to-sequence model on both tasks.

twitter-based traffic information system based on vector representations for words

recently, researchers have shown an increased interest in harnessing twitter data for dynamic monitoring of traffic conditions. bag-of-words representation is a common method in literature for tweet modeling and retrieving traffic information, yet it suffers from the curse of dimensionality and sparsity. to address these issues, our specific objective is to propose a simple and robust framework on the top of word embedding for distinguishing traffic-related tweets against non-traffic-related ones. in our proposed model, a tweet is classified as traffic-related if semantic similarity between its words and a small set of traffic keywords exceeds a threshold value. semantic similarity between words is captured by means of word-embedding models, which is an unsupervised learning tool. the proposed model is as simple as having only one trainable parameter. the model takes advantage of outstanding merits, which are demonstrated through several evaluation steps. the state-of-the-art test accuracy for our proposed model is 95.9%.

set cross entropy: likelihood-based permutation invariant loss function for probability distributions

we propose a permutation-invariant loss function designed for the neural networks reconstructing a set of elements without considering the order within its vector representation. unlike popular approaches for encoding and decoding a set, our work does not rely on a carefully engineered network topology nor by any additional sequential algorithm. the proposed method, set cross entropy, has a natural information-theoretic interpretation and is related to the metrics defined for sets. we evaluate the proposed approach in two object reconstruction tasks and a rule learning task.

ladder networks for semi-supervised hyperspectral image classification

we used the ladder network [rasmus et al. (2015)] to perform hyperspectral image classification in a semi-supervised setting. the ladder network distinguishes itself from other semi-supervised methods by jointly optimizing a supervised and unsupervised cost. in many settings this has proven to be more successful than other semi-supervised techniques, such as pretraining using unlabeled data. we furthermore show that the convolutional ladder network outperforms most of the current techniques used in hyperspectral image classification and achieves new state-of-the-art performance on the pavia university dataset given only 5 labeled data points per class.

learning vine copula models for synthetic data generation

a vine copula model is a flexible high-dimensional dependence model which uses only bivariate building blocks. however, the number of possible configurations of a vine copula grows exponentially as the number of variables increases, making model selection a major challenge in development. in this work, we formulate a vine structure learning problem with both vector and reinforcement learning representation. we use neural network to find the embeddings for the best possible vine model and generate a structure. throughout experiments on synthetic and real-world datasets, we show that our proposed approach fits the data better in terms of log-likelihood. moreover, we demonstrate that the model is able to generate high-quality samples in a variety of applications, making it a good candidate for synthetic data generation.

phase retrieval by alternating minimization with random initialization

we consider a phase retrieval problem, where the goal is to reconstruct a $n$-dimensional complex vector from its phaseless scalar products with $m$ sensing vectors, independently sampled from complex normal distributions. we show that, with a random initialization, the classical algorithm of alternating minimization succeeds with high probability as $n,m\rightarrow\infty$ when ${m}/{\log^3m}\geq mn^{3/2}\log^{1/2}n$ for some $m>0$. this is a step toward proving the conjecture in \cite{waldspurger2016}, which conjectures that the algorithm succeeds when $m=o(n)$. the analysis depends on an approach that enables the decoupling of the dependency between the algorithmic iterates and the sensing vectors.

singing voice separation using a deep convolutional neural network trained by ideal binary mask and cross entropy

separating a singing voice from its music accompaniment remains an important challenge in the field of music information retrieval. we present a unique neural network approach inspired by a technique that has revolutionized the field of vision: pixel-wise image classification, which we combine with cross entropy loss and pretraining of the cnn as an autoencoder on singing voice spectrograms. the pixel-wise classification technique directly estimates the sound source label for each time-frequency (t-f) bin in our spectrogram image, thus eliminating common pre- and postprocessing tasks. the proposed network is trained by using the ideal binary mask (ibm) as the target output label. the ibm identifies the dominant sound source in each t-f bin of the magnitude spectrogram of a mixture signal, by considering each t-f bin as a pixel with a multi-label (for each sound source). cross entropy is used as the training objective, so as to minimize the average probability error between the target and predicted label for each pixel. by treating the singing voice separation problem as a pixel-wise classification task, we additionally eliminate one of the commonly used, yet not easy to comprehend, postprocessing steps: the wiener filter postprocessing. the proposed cnn outperforms the first runner up in the music information retrieval evaluation exchange (mirex) 2016 and the winner of mirex 2014 with a gain of 2.2702 ~ 5.9563 db global normalized source to distortion ratio (gnsdr) when applied to the ikala dataset. an experiment with the dsd100 dataset on the full-tracks song evaluation task also shows that our model is able to compete with cutting-edge singing voice separation systems which use multi-channel modeling, data augmentation, and model blending.

understanding information centrality metric: a simulation approach

identifying the central people in information flow networks is essential to understanding how people communicate and coordinate as well as who controls the information flows in the network. however, the appropriate usage of centrality metrics depends on an understanding of the type of network flow. networks can vary in the way node-to-node transmission takes place, or in the way a course through the network is taken, thereby leading to different types of information flow processes. when metrics are used for an inappropriate flow process, the result of the metric can be misleading and often incorrect. in this paper we create a simulation of the flow of information in a network, and then we investigate the relation of information centrality as well as other network centralities, like betweenness, closeness and eigenvector along with the outcome of simulations with information flowing through walks rather than paths, trails or geodesics. we find that information centrality is more similar to eigenvector and degree centrality than to closeness centrality as postulated by previous literature. we also find an interesting pattern emerge from the inter metric correlations.

statistics with improper posteriors

in 1933 kolmogorov constructed a general theory that defines the modern concept of conditional probability. in 1955 renyi fomulated a new axiomatic theory for probability motivated by the need to include unbounded measures. we introduce a general concept of conditional probability in renyi spaces. in this theory improper priors are allowed, and the resulting posteriors can also be improper.

local average treatment effects estimation via substantive model compatible multiple imputation

non-adherence to assigned treatment is common in randomised controlled trials (rcts). recently, there has been an increased interest in estimating causal effects of treatment received, for example the so-called local average treatment effect (late). instrumental variables (iv) methods can be used for identification, with estimation proceeding either via fully parametric mixture models or two-stage least squares (tsls). tsls is popular but can be problematic for binary outcomes where the estimand of interest is a causal odds ratio. mixture models are rarely used in practice, perhaps because of their perceived complexity and need for specialist software. here, we propose using multiple imputation (mi) to impute the latent compliance class appearing in the mixture models. since such models include an interaction term between compliance class and randomised treatment, we use `substantive model compatible' mi (smc mic), which can also address other missing data, before fitting the mixture models via maximum likelihood to the mi datasets and combining results via rubin's rules. we use simulations to compare the performance of smc mic to existing approaches and also illustrate the methods by re-analysing a rct in uk primary health. we show that smc mic can be more efficient than full bayesian estimation when auxiliary variables are incorporated, and is superior to two-stage methods, especially for binary outcomes.

estimating cluster-level local average treatment effects in cluster randomised trials with non-adherence

non-adherence to assigned treatment is a common issue in cluster randomised trials (crts). in these settings, the efficacy estimand may be also of interest. many methodological contributions in recent years have advocated using instrumental variables to identify and estimate the local average treatment effect (late). however, the clustered nature of randomisation in crts adds to the complexity of such analyses. in this paper, we show that under certain assumptions, the late can be estimated via two-stage least squares (tsls) using cluster-level summaries of outcomes and treatment received. implementation needs to account for this, as well as the possible heteroscedasticity, to obtain valid inferences. we use simulations to assess the performance of tsls of cluster-level summaries under cluster-level or individual-level non-adherence, with and without weighting and robust standard errors. we also explore the impact of adjusting for cluster-level covariates and of appropriate degrees of freedom correction for inference. we find that tsls estimation using cluster-level summaries provides estimates with small to negligible bias and coverage close to nominal level, provided small sample degrees of freedom correction is used for inference, with appropriate use of robust standard errors. we illustrate the methods by re-analysing a crt in uk primary health settings.

the lagrange approach in the monotone single index model

the finite-dimensional parameters of the monotone single index model are often estimated by minimization of a least squares criterion and reparametrization to deal with the non-unicity. we avoid the reparametrization by using a lagrange-type method and replace the minimization over the finite-dimensional parameter alpha by a `crossing of zero' criterion at the derivative level. in particular, we consider a simple score estimator (sse), an efficient score estimator (ese), and a penalized least squares estimator (plse) for which we can apply this method. the sse and ese were discussed in balabdaoui, groeneboom and hendrickx (2018}, but the proofs still used reparametrization. another version of the plse was discussed in kuchibhotla and patra (2017), where also reparametrization was used. the estimators are compared with the profile least squares estimator (lse), han's maximum rank estimator (mre), the effective dimension reduction estimator (edr) and a linear least squares estimator, which can be used if the covariates have an elliptically symmetric distribution. we also investigate the effects of random starting values in the search algorithms.

bad practices in evaluation methodology relevant to class-imbalanced problems

for research to go in the right direction, it is essential to be able to compare and quantify performance of different algorithms focused on the same problem. choosing a suitable evaluation metric requires deep understanding of the pursued task along with all of its characteristics. we argue that in the case of applied machine learning, proper evaluation metric is the basic building block that should be in the spotlight and put under thorough examination. here, we address tasks with class imbalance, in which the class of interest is the one with much lower number of samples. we encountered non-insignificant amount of recent papers, in which improper evaluation methods are used, borrowed mainly from the field of balanced problems. such bad practices may heavily bias the results in favour of inappropriate algorithms and give false expectations of the state of the field.

a multi-class structured dictionary learning method using discriminant atom selection

in the last decade, traditional dictionary learning methods have been successfully applied to various pattern classification tasks. although these methods produce sparse representations of signals which are robust against distortions and missing data, such representations quite often turn out to be unsuitable if the final objective is signal classification. in order to overcome or at least to attenuate such a weakness, several new methods which incorporate discriminative information into sparse-inducing models have emerged in recent years. in particular, methods for discriminative dictionary learning have shown to be more accurate (in terms of signal classification) than the traditional ones, which are only focused on minimizing the total representation error. in this work, we present both a novel multi-class discriminative measure and an innovative dictionary learning method. for a given dictionary, this new measure, which takes into account not only when a particular atom is used for representing signals coming from a certain class and the magnitude of its corresponding representation coefficient, but also the effect that such an atom has in the total representation error, is capable of efficiently quantifying the degree of discriminability of each one of the atoms. on the other hand, the new dictionary construction method yields dictionaries which are highly suitable for multi-class classification tasks. our method was tested with a widely used database for handwritten digit recognition and compared with three state-of-the-art classification methods. the results show that our method significantly outperforms the other three achieving good recognition rates and additionally, reducing the computational cost of the classifier.

compressive classification (machine learning without learning)

compressive learning is a framework where (so far unsupervised) learning tasks use not the entire dataset but a compressed summary (sketch) of it. we propose a compressive learning classification method, and a novel sketch function for images.

necessary and probably sufficient test for finding valid instrumental variables

can instrumental variables be found from data? while instrumental variable (iv) methods are widely used to identify causal effect, testing their validity from observed data remains a challenge. this is because validity of an iv depends on two assumptions, exclusion and as-if-random, that are largely believed to be untestable from data. in this paper, we show that under certain conditions, testing for instrumental variables is possible. we build upon prior work on necessary tests to derive a test that characterizes the odds of being a valid instrument, thus yielding the name "necessary and probably sufficient". the test works by defining the class of invalid-iv and valid-iv causal models as bayesian generative models and comparing their marginal likelihood based on observed data. when all variables are discrete, we also provide a method to efficiently compute these marginal likelihoods. we evaluate the test on an extensive set of simulations for binary data, inspired by an open problem for iv testing proposed in past work. we find that the test is most powerful when an instrument follows monotonicity---effect on treatment is either non-decreasing or non-increasing---and has moderate-to-weak strength; incidentally, such instruments are commonly used in observational studies. among as-if-random and exclusion, it detects exclusion violations with higher power. applying the test to ivs from two seminal studies on instrumental variables and five recent studies from the american economic review shows that many of the instruments may be flawed, at least when all variables are discretized. the proposed test opens the possibility of data-driven validation and search for instrumental variables.

automatic salt deposits segmentation: a deep learning approach

one of the most important applications of seismic reflection is the hydrocarbon exploration which is closely related to salt deposits analysis. this problem is very important even nowadays due to it's non-linear nature. taking into account the recent developments in deep learning networks tgs-nopec geophysical company hosted the kaggle competition for salt deposits segmentation problem in seismic image data. in this paper, we demonstrate the great performance of several novel deep learning techniques merged into a single neural network which achieved the 27th place (top 1%) in the mentioned competition. using a u-net with resnext-50 encoder pre-trained on imagenet as our base architecture, we implemented spatial-channel squeeze & excitation, lovasz loss, coordconv and hypercolumn methods. the source code for our solution is made publicly available at https://github.com/k-mike/automatic-salt-deposits-segmentation.

matrix factorization via deep learning

matrix completion is one of the key problems in signal processing and machine learning. in recent years, deep-learning-based models have achieved state-of-the-art results in matrix completion. nevertheless, they suffer from two drawbacks: (i) they can not be extended easily to rows or columns unseen during training; and (ii) their results are often degraded in case discrete predictions are required. this paper addresses these two drawbacks by presenting a deep matrix factorization model and a generic method to allow joint training of the factorization model and the discretization operator. experiments on a real movie rating dataset show the efficacy of the proposed models.

privacy-preserving distributed deep learning for clinical data

deep learning with medical data often requires larger samples sizes than are available at single providers. while data sharing among institutions is desirable to train more accurate and sophisticated models, it can lead to severe privacy concerns due the sensitive nature of the data. this problem has motivated a number of studies on distributed training of neural networks that do not require direct sharing of the training data. however, simple distributed training does not offer provable privacy guarantees to satisfy technical safe standards and may reveal information about the underlying patients. we present a method to train neural networks for clinical data in a distributed fashion under differential privacy. we demonstrate these methods on two datasets that include information from multiple independent sites, the eicu collaborative research database and the cancer genome atlas.

natural option critic

the recently proposed option-critic architecture bacon et al. provide a stochastic policy gradient approach to hierarchical reinforcement learning. specifically, they provide a way to estimate the gradient of the expected discounted return with respect to parameters that define a finite number of temporally extended actions, called \textit{options}. in this paper we show how the option-critic architecture can be extended to estimate the natural gradient of the expected discounted return. to this end, the central questions that we consider in this paper are: 1) what is the definition of the natural gradient in this context, 2) what is the fisher information matrix associated with an option's parameterized policy, 3) what is the fisher information matrix associated with an option's parameterized termination function, and 4) how can a compatible function approximation approach be leveraged to obtain natural gradient estimates for both the parameterized policy and parameterized termination functions of an option with per-time-step time and space complexity linear in the total number of parameters. based on answers to these questions we introduce the natural option critic algorithm. experimental results showcase improvement over the vanilla gradient approach.

expanding search in the space of empirical ml

as researchers and practitioners of applied machine learning, we are given a set of requirements on the problem to be solved, the plausibly obtainable data, and the computational resources available. we aim to find (within those bounds) reliably useful combinations of problem, data, and algorithm. an emphasis on algorithmic or technical novelty in ml conference publications leads to exploration of one dimension of this space. data collection and ml deployment at scale in industry settings offers an environment for exploring the others. our conferences and reviewing criteria can better support empirical ml by soliciting and incentivizing experimentation and synthesis independent of algorithmic innovation.

a system for efficient communication between patients and pharmacies

when studying human-technology interaction systems, researchers thrive to achieve intuitiveness and facilitate the people's life through a thoughtful and in-depth study of several components of the application system that supports some particular business communication with customers. particularly in the healthcare field, some requirements such as clarity, transparency, efficiency, and speed in transmitting information to patients and or healthcare professionals might mean an important increase in the well-being of the patient and productivity of the healthcare professional. in this work, the authors study the difficulties patients frequently have when communicating with pharmacists. in addition to a statistical study of a survey conducted with more than two hundred frequent pharmacy customers, we propose an it solution for better communication between patients and pharmacists.

parallelising particle filters with butterfly interactions

bootstrap particle filter (bpf) is the corner stone of many popular algorithms used for solving inference problems involving time series that are observed through noisy measurements in a non-linear and non-gaussian context. the long term stability of bpf arises from particle interactions which in the context of modern parallel computing systems typically means that particle information needs to be communicated between processing elements, which makes parallel implementation of bpf nontrivial. in this paper we show that it is possible to constrain the interactions in a way which, under some assumptions, enables the reduction of the cost of communicating the particle information while still preserving the consistency and the long term stability of the bpf. numerical experiments demonstrate that although the imposed constraints introduce additional error, the proposed method shows potential to be the method of choice in certain settings.

particle identification in ground-based gamma-ray astronomy using convolutional neural networks

modern detectors of cosmic gamma-rays are a special type of imaging telescopes (air cherenkov telescopes) supplied with cameras with a relatively large number of photomultiplier-based pixels. for example, the camera of the taiga-iact telescope has 560 pixels of hexagonal structure. images in such cameras can be analysed by deep learning techniques to extract numerous physical and geometrical parameters and/or for incoming particle identification. the most powerful deep learning technique for image analysis, the so-called convolutional neural network (cnn), was implemented in this study. two open source libraries for machine learning, pytorch and tensorflow, were tested as possible software platforms for particle identification in imaging air cherenkov telescopes. monte carlo simulation was performed to analyse images of gamma-rays and background particles (protons) as well as estimate identification accuracy. further steps of implementation and improvement of this technique are discussed.

batch selection for parallelisation of bayesian quadrature

integration over non-negative integrands is a central problem in machine learning (e.g. for model averaging, (hyper-)parameter marginalisation, and computing posterior predictive distributions). bayesian quadrature is a probabilistic numerical integration technique that performs promisingly when compared to traditional markov chain monte carlo methods. however, in contrast to easily-parallelised mcmc methods, bayesian quadrature methods have, thus far, been essentially serial in nature, selecting a single point to sample at each step of the algorithm. we deliver methods to select batches of points at each step, based upon those recently presented in the batch bayesian optimisation literature. such parallelisation significantly reduces computation time, especially when the integrand is expensive to sample.

generating high fidelity images with subscale pixel networks and multidimensional upscaling

the unconditional generation of high fidelity images is a longstanding benchmark for testing the performance of image decoders. autoregressive image models have been able to generate small images unconditionally, but the extension of these methods to large images where fidelity can be more readily assessed has remained an open problem. among the major challenges are the capacity to encode the vast previous context and the sheer difficulty of learning a distribution that preserves both global semantic coherence and exactness of detail. to address the former challenge, we propose the subscale pixel network (spn), a conditional decoder architecture that generates an image as a sequence of sub-images of equal size. the spn compactly captures image-wide spatial dependencies and requires a fraction of the memory and the computation required by other fully autoregressive models. to address the latter challenge, we propose to use multidimensional upscaling to grow an image in both size and depth via intermediate stages utilising distinct spns. we evaluate spns on the unconditional generation of celebahq of size 256 and of imagenet from size 32 to 256. we achieve state-of-the-art likelihood results in multiple settings, set up new benchmark results in previously unexplored settings and are able to generate very high fidelity large scale samples on the basis of both datasets.

approximating the solution to wave propagation using deep neural networks

humans gain an implicit understanding of physical laws through observing and interacting with the world. endowing an autonomous agent with an understanding of physical laws through experience and observation is seldom practical: we should seek alternatives. fortunately, many of the laws of behaviour of the physical world can be derived from prior knowledge of dynamical systems, expressed through the use of partial differential equations. in this work, we suggest a neural network capable of understanding a specific physical phenomenon: wave propagation in a two-dimensional medium. we define `understanding' in this context as the ability to predict the future evolution of the spatial patterns of rendered wave amplitude from a relatively small set of initial observations. the inherent complexity of the wave equations -- together with the existence of reflections and interference -- makes the prediction problem non-trivial. a network capable of making approximate predictions also unlocks the opportunity to speed-up numerical simulations for wave propagation. to this aim, we created a novel dataset of simulated wave motion and built a predictive deep neural network comprising of three main blocks: an encoder, a propagator made by 3 lstms, and a decoder. results show reasonable predictions for as long as 80 time steps into the future on a dataset not seen during training. furthermore, the network is able to generalize to an initial condition that is qualitatively different from those seen during training.

rigorous agent evaluation: an adversarial approach to uncover catastrophic failures

this paper addresses the problem of evaluating learning systems in safety critical domains such as autonomous driving, where failures can have catastrophic consequences. we focus on two problems: searching for scenarios when learned agents fail and assessing their probability of failure. the standard method for agent evaluation in reinforcement learning, vanilla monte carlo, can miss failures entirely, leading to the deployment of unsafe agents. we demonstrate this is an issue for current agents, where even matching the compute used for training is sometimes insufficient for evaluation. to address this shortcoming, we draw upon the rare event probability estimation literature and propose an adversarial evaluation approach. our approach focuses evaluation on adversarially chosen situations, while still providing unbiased estimates of failure probabilities. the key difficulty is in identifying these adversarial situations -- since failures are rare there is little signal to drive optimization. to solve this we propose a continuation approach that learns failure modes in related but less robust agents. our approach also allows reuse of data already collected for training the agent. we demonstrate the efficacy of adversarial evaluation on two standard domains: humanoid control and simulated driving. experimental results show that our methods can find catastrophic failures and estimate failures rates of agents multiple orders of magnitude faster than standard evaluation schemes, in minutes to hours rather than days.

a pixel-based framework for data-driven clothing

with the aim of creating virtual cloth deformations more similar to real world clothing, we propose a new computational framework that recasts three dimensional cloth deformation as an rgb image in a two dimensional pattern space. then a three dimensional animation of cloth is equivalent to a sequence of two dimensional rgb images, which in turn are driven/choreographed via animation parameters such as joint angles. this allows us to leverage popular cnns to learn cloth deformations in image space. the two dimensional cloth pixels are extended into the real world via standard body skinning techniques, after which the rgb values are interpreted as texture offsets and displacement maps. notably, we illustrate that our approach does not require accurate unclothed body shapes or robust skinning techniques. additionally, we discuss how standard image based techniques such as image partitioning for higher resolution, gans for merging partitioned image regions back together, etc., can readily be incorporated into our framework.

general-to-detailed gan for infrequent class medical images

deep learning has significant potential for medical imaging. however, since the incident rate of each disease varies widely, the frequency of classes in a medical image dataset is imbalanced, leading to poor accuracy for such infrequent classes. one possible solution is data augmentation of infrequent classes using synthesized images created by generative adversarial networks (gans), but conventional gans also require certain amount of images to learn. to overcome this limitation, here we propose general-to-detailed gan (gdgan), serially connected two gans, one for general labels and the other for detailed labels. gdgan produced diverse medical images, and the network trained with an augmented dataset outperformed other networks using existing methods with respect to area-under-curve (auc) of receiver operating characteristic (roc) curve.

learning individualized cardiovascular responses from large-scale wearable sensors data

we consider the problem of modeling cardiovascular responses to physical activity and sleep changes captured by wearable sensors in free living conditions. we use an attentional convolutional neural network to learn parsimonious signatures of individual cardiovascular response from data recorded at the minute level resolution over several months on a cohort of 80k people. we demonstrate internal validity by showing that signatures generated on an individual's 2017 data generalize to predict minute-level heart rate from physical activity and sleep for the same individual in 2018, outperforming several time-series forecasting baselines. we also show external validity demonstrating that signatures outperform plain resting heart rate (rhr) in predicting variables associated with cardiovascular functions, such as age and body mass index (bmi). we believe that the computed cardiovascular signatures have utility in monitoring cardiovascular health over time, including detecting abnormalities and quantifying recovery from acute events.

assigning a grade: accurate measurement of road quality using satellite imagery

roads are critically important infrastructure to societal and economic development, with huge investments made by governments every year. however, methods for monitoring those investments tend to be time-consuming, laborious, and expensive, placing them out of reach for many developing regions. in this work, we develop a model for monitoring the quality of road infrastructure using satellite imagery. for this task, we harness two trends: the increasing availability of high-resolution, often-updated satellite imagery, and the enormous improvement in speed and accuracy of convolutional neural network-based methods for performing computer vision tasks. we employ a unique dataset of road quality information on 7000km of roads in kenya combined with 50cm resolution satellite imagery. we create models for a binary classification task as well as a comprehensive 5-category classification task, with accuracy scores of 88 and 73 percent respectively. we also provide evidence of the robustness of our methods with challenging held-out scenarios, though we note some improvement is still required for confident analysis of a never before seen road. we believe these results are well-positioned to have substantial impact on a broad set of transport applications.

gantruth - an unpaired image-to-image translation method for driving scenarios

synthetic image translation has significant potentials in autonomous transportation systems. that is due to the expense of data collection and annotation as well as the unmanageable diversity of real-words situations. the main issue with unpaired image-to-image translation is the ill-posed nature of the problem. in this work, we propose a novel method for constraining the output space of unpaired image-to-image translation. we make the assumption that the environment of the source domain is known (e.g. synthetically generated), and we propose to explicitly enforce preservation of the ground-truth labels on the translated images. we experiment on preserving ground-truth information such as semantic segmentation, disparity, and instance segmentation. we show significant evidence that our method achieves improved performance over the state-of-the-art model of unit for translating images from synthia to cityscapes. the generated images are perceived as more realistic in human surveys and outperforms unit when used in a domain adaptation scenario for semantic segmentation.

a graph-cnn for 3d point cloud classification

graph convolutional neural networks (graph-cnns) extend traditional cnns to handle data that is supported on a graph. major challenges when working with data on graphs are that the support set (the vertices of the graph) do not typically have a natural ordering, and in general, the topology of the graph is not regular (i.e., vertices do not all have the same number of neighbors). thus, graph-cnns have huge potential to deal with 3d point cloud data which has been obtained from sampling a manifold. in this paper, we develop a graph-cnn for classifying 3d point cloud data, called pointgcn. the architecture combines localized graph convolutions with two types of graph downsampling operations (also known as pooling). by the effective exploration of the point cloud local structure using the graph-cnn, the proposed architecture achieves competitive performance on the 3d object classification benchmark modelnet, and our architecture is more stable than competing schemes.

multiview based 3d scene understanding on partial point sets

deep learning within the context of point clouds has gained much research interest in recent years mostly due to the promising results that have been achieved on a number of challenging benchmarks, such as 3d shape recognition and scene semantic segmentation. in many realistic settings however, snapshots of the environment are often taken from a single view, which only contains a partial set of the scene due to the field of view restriction of commodity cameras. 3d scene semantic understanding on partial point clouds is considered as a challenging task. in this work, we propose a processing approach for 3d point cloud data based on a multiview representation of the existing 360{\deg} point clouds. by fusing the original 360{\deg} point clouds and their corresponding 3d multiview representations as input data, a neural network is able to recognize partial point sets while improving the general performance on complete point sets, resulting in an overall increase of 31.9% and 4.3% in segmentation accuracy for partial and complete scene semantic understanding, respectively. this method can also be applied in a wider 3d recognition context such as 3d part segmentation.

finefool: fine object contour attack via attention

machine learning models have been shown vulnerable to adversarial attacks launched by adversarial examples which are carefully crafted by attacker to defeat classifiers. deep learning models cannot escape the attack either. most of adversarial attack methods are focused on success rate or perturbations size, while we are more interested in the relationship between adversarial perturbation and the image itself. in this paper, we put forward a novel adversarial attack based on contour, named finefool. finefool not only has better attack performance compared with other state-of-art white-box attacks in aspect of higher attack success rate and smaller perturbation, but also capable of visualization the optimal adversarial perturbation via attention on object contour. to the best of our knowledge, finefool is for the first time combines the critical feature of the original clean image with the optimal perturbations in a visible manner. inspired by the correlations between adversarial perturbations and object contour, slighter perturbations is produced via focusing on object contour features, which is more imperceptible and difficult to be defended, especially network add-on defense methods with the trade-off between perturbations filtering and contour feature loss. compared with existing state-of-art attacks, extensive experiments are conducted to show that finefool is capable of efficient attack against defensive deep models.

learning to unlearn: building immunity to dataset bias in medical imaging studies

medical imaging machine learning algorithms are usually evaluated on a single dataset. although training and testing are performed on different subsets of the dataset, models built on one study show limited capability to generalize to other studies. while database bias has been recognized as a serious problem in the computer vision community, it has remained largely unnoticed in medical imaging research. transfer learning thus remains confined to the re-use of feature representations requiring re-training on the new dataset. as a result, machine learning models do not generalize even when trained on imaging datasets that were captured to study the same variable of interest. the ability to transfer knowledge gleaned from one study to another, without the need for re-training, if possible, would provide reassurance that the models are learning knowledge fundamental to the problem under study instead of latching onto the idiosyncracies of a dataset. in this paper, we situate the problem of dataset bias in the context of medical imaging studies. we show empirical evidence that such a problem exists in medical datasets. we then present a framework to unlearn study membership as a means to handle the problem of database bias. our main idea is to take the data from the original feature space to an intermediate space where the data points are indistinguishable in terms of which study they come from, while maintaining the recognition capability with respect to the variable of interest. this will promote models which learn the more general properties of the etiology under study instead of aligning to dataset-specific peculiarities. essentially, our proposed model learns to unlearn the dataset bias.

deep learning for classical japanese literature

much of machine learning research focuses on producing models which perform well on benchmark tasks, in turn improving our understanding of the challenges associated with those tasks. from the perspective of ml researchers, the content of the task itself is largely irrelevant, and thus there have increasingly been calls for benchmark tasks to more heavily focus on problems which are of social or cultural relevance. in this work, we introduce kuzushiji-mnist, a dataset which focuses on kuzushiji (cursive japanese), as well as two larger, more challenging datasets, kuzushiji-49 and kuzushiji-kanji. through these datasets, we wish to engage the machine learning community into the world of classical japanese literature. dataset available at https://github.com/rois-codh/kmnist

learning cheap and novel flight itineraries

we consider the problem of efficiently constructing cheap and novel round trip flight itineraries by combining legs from different airlines. we analyse the factors that contribute towards the price of such itineraries and find that many result from the combination of just 30% of airlines and that the closer the departure of such itineraries is to the user's search date the more likely they are to be cheaper than the tickets from one airline. we use these insights to formulate the problem as a trade-off between the recall of cheap itinerary constructions and the costs associated with building them. we propose a supervised learning solution with location embeddings which achieves an auc=80.48, a substantial improvement over simpler baselines. we discuss various practical considerations for dealing with the staleness and the stability of the model and present the design of the machine learning pipeline. finally, we present an analysis of the model's performance in production and its impact on skyscanner's users.

robuststl: a robust seasonal-trend decomposition algorithm for long time series

decomposing complex time series into trend, seasonality, and remainder components is an important task to facilitate time series anomaly detection and forecasting. although numerous methods have been proposed, there are still many time series characteristics exhibiting in real-world data which are not addressed properly, including 1) ability to handle seasonality fluctuation and shift, and abrupt change in trend and reminder; 2) robustness on data with anomalies; 3) applicability on time series with long seasonality period. in the paper, we propose a novel and generic time series decomposition algorithm to address these challenges. specifically, we extract the trend component robustly by solving a regression problem using the least absolute deviations loss with sparse regularization. based on the extracted trend, we apply the the non-local seasonal filtering to extract the seasonality component. this process is repeated until accurate decomposition is obtained. experiments on different synthetic and real-world time series datasets demonstrate that our method outperforms existing solutions.

voice disorder detection using long short term memory (lstm) model

automated detection of voice disorders with computational methods is a recent research area in the medical domain since it requires a rigorous endoscopy for the accurate diagnosis. efficient screening methods are required for the diagnosis of voice disorders so as to provide timely medical facilities in minimal resources. detecting voice disorder using computational methods is a challenging problem since audio data is continuous due to which extracting relevant features and applying machine learning is hard and unreliable. this paper proposes a long short term memory model (lstm) to detect pathological voice disorders and evaluates its performance in a real 400 testing samples without any labels. different feature extraction methods are used to provide the best set of features before applying lstm model for classification. the paper describes the approach and experiments that show promising results with 22% sensitivity, 97% specificity and 56% unweighted average recall.

density deconvolution with additive measurement errors using quadratic programming

distribution estimation for noisy data via density deconvolution is a notoriously difficult problem for typical noise distributions like gaussian. we develop a density deconvolution estimator based on quadratic programming (qp) that can achieve better estimation than kernel density deconvolution methods. the qp approach appears to have a more favorable regularization tradeoff between oversmoothing vs. oscillation, especially at the tails of the distribution. an additional advantage is that it is straightforward to incorporate a number of common density constraints such as nonnegativity, integration-to-one, unimodality, tail convexity, tail monotonicity, and support constraints. we demonstrate that the qp approach has outstanding estimation performance relative to existing methods. its performance is superior when only the universally applicable nonnegativity and integration-to-one constraints are incorporated, and incorporating additional common constraints when applicable (e.g., nonnegative support, unimodality, tail monotonicity or convexity, etc.) can further substantially improve the estimation.

effects of transit signal priority on traffic safety: interrupted time series analysis of portland, oregon, implementations

transit signal priority (tsp) has been implemented to transit systems in many cities of the united states. in evaluating tsp systems, more attention has been given to its operational effects than to its safety effects. existing studies assessing safety effects of tsp reported mixed results, indicating that the safety effects of tsp vary in different contexts. in this study, tsp implementations in portland, oregon, were assessed using interrupted time series analysis (itsa) on month-to-month changes in number of crashes from january 1995 to december 2010. single-group and controlled itsa were conducted for all crashes, property-damage-only crashes, fatal and injury crashes, pedestrian-involved crashes, and bike-involved crashes. evaluation of the post-intervention period (2003 to 2010) showed a reduction in all crashes on street sections with tsp (-4.5 percent), comparing with the counterfactual estimations based on the control group data. the reduction in property-damage-only crashes (-10.0 percent) contributed the most to the overall reduction. fatal and injury crashes leveled out after tsp implementation but did not change significantly comparing with the control group. pedestrian and bike-involved crashes were found to increase in the post-intervention period with tsp, comparing with the control group. potential reasons to these tsp effects on traffic safety were discussed.

uncertainty sampling is preconditioned stochastic gradient descent on zero-one loss

uncertainty sampling, a popular active learning algorithm, is used to reduce the amount of data required to learn a classifier, but it has been observed in practice to converge to different parameters depending on the initialization and sometimes to even better parameters than standard training on all the data. in this work, we give a theoretical explanation of this phenomenon, showing that uncertainty sampling on a convex loss can be interpreted as performing a preconditioned stochastic gradient step on a smoothed version of the population zero-one loss that converges to the population zero-one loss. furthermore, uncertainty sampling moves in a descent direction and converges to stationary points of the smoothed population zero-one loss. experiments on synthetic and real datasets support this connection.

regularized ensembles and transferability in adversarial learning

despite the considerable success of convolutional neural networks in a broad array of domains, recent research has shown these to be vulnerable to small adversarial perturbations, commonly known as adversarial examples. moreover, such examples have shown to be remarkably portable, or transferable, from one model to another, enabling highly successful black-box attacks. we explore this issue of transferability and robustness from two dimensions: first, considering the impact of conventional $l_p$ regularization as well as replacing the top layer with a linear support vector machine (svm), and second, the value of combining regularized models into an ensemble. we show that models trained with different regularizers present barriers to transferability, as does partial information about the models comprising the ensemble.

improving similarity search with high-dimensional locality-sensitive hashing

we propose a new class of data-independent locality-sensitive hashing (lsh) algorithms based on the fruit fly olfactory circuit. the fundamental difference of this approach is that, instead of assigning hashes as dense points in a low dimensional space, hashes are assigned in a high dimensional space, which enhances their separability. we show theoretically and empirically that this new family of hash functions is locality-sensitive and preserves rank similarity for inputs in any `p space. we then analyze different variations on this strategy and show empirically that they outperform existing lsh methods for nearest-neighbors search on six benchmark datasets. finally, we propose a multi-probe version of our algorithm that achieves higher performance for the same query time, or conversely, that maintains performance of prior approaches while taking significantly less indexing time and memory. overall, our approach leverages the advantages of separability provided by high-dimensional spaces, while still remaining computationally efficient

bayesian spatial inversion and conjugate selection gaussian prior models

we introduce the concept of conjugate prior models for a given likelihood function in bayesian spatial inversion. the conjugate class of prior models can be selection extended and still remain conjugate. we demonstrate the generality of selection gaussian prior models, representing multi-modality, skewness and heavy-tailedness. for gauss-linear likelihood functions, the posterior model is also selection gaussian. the model parameters of the posterior pdf are explisite functions of the model parameters of the likelihood and prior models - and the actual observations, of course. efficient algorithms for simulation of and prediction for the selection gaussian posterior pdf are defined. inference of the model parameters in the selection gaussian prior pdf, based on one training image of the spatial variable, can be reliably made by a maximum likelihood criterion and numerical optimization. lastly, a seismic inversion case study is presented, and improvements of $ 20$-$40\%$ in prediction mean-square-error, relative to traditional gaussian inversion, are found.

less but better: generalization enhancement of ordinal embedding via distributional margin

in the absence of prior knowledge, ordinal embedding methods obtain new representation for items in a low-dimensional euclidean space via a set of quadruple-wise comparisons. these ordinal comparisons often come from human annotators, and sufficient comparisons induce the success of classical approaches. however, collecting a large number of labeled data is known as a hard task, and most of the existing work pay little attention to the generalization ability with insufficient samples. meanwhile, recent progress in large margin theory discloses that rather than just maximizing the minimum margin, both the margin mean and variance, which characterize the margin distribution, are more crucial to the overall generalization performance. to address the issue of insufficient training samples, we propose a margin distribution learning paradigm for ordinal embedding, entitled distributional margin based ordinal embedding (\textit{dmoe}). precisely, we first define the margin for ordinal embedding problem. secondly, we formulate a concise objective function which avoids maximizing margin mean and minimizing margin variance directly but exhibits the similar effect. moreover, an augmented lagrange multiplier based algorithm is customized to seek the optimal solution of \textit{dmoe} effectively. experimental studies on both simulated and real-world datasets are provided to show the effectiveness of the proposed algorithm.

anomaly detection for network connection logs

we leverage a streaming architecture based on elk, spark and hadoop in order to collect, store, and analyse database connection logs in near real-time. the proposed system investigates outliers using unsupervised learning; widely adopted clustering and classification algorithms for log data, highlighting the subtle variances in each model by visualisation of outliers. arriving at a novel solution to evaluate untagged, unfiltered connection logs, we propose an approach that can be extrapolated to a generalised system of analysing connection logs across a large infrastructure comprising thousands of individual nodes and generating hundreds of lines in logs per second.

robust ordinal embedding from contaminated relative comparisons

existing ordinal embedding methods usually follow a two-stage routine: outlier detection is first employed to pick out the inconsistent comparisons; then an embedding is learned from the clean data. however, learning in a multi-stage manner is well-known to suffer from sub-optimal solutions. in this paper, we propose a unified framework to jointly identify the contaminated comparisons and derive reliable embeddings. the merits of our method are three-fold: (1) by virtue of the proposed unified framework, the sub-optimality of traditional methods is largely alleviated; (2) the proposed method is aware of global inconsistency by minimizing a corresponding cost, while traditional methods only involve local inconsistency; (3) instead of considering the nuclear norm heuristics, we adopt an exact solution for rank equality constraint. our studies are supported by experiments with both simulated examples and real-world data. the proposed framework provides us a promising tool for robust ordinal embedding from the contaminated comparisons.

least absolute deviations uncertain regression with imprecise observations

traditionally regression analysis answers questions about the relationships among variables based on the assumption that the observation values of variables are precise numbers. it has long been dominated by least squares techniques, mostly due to their elegant theoretical foundation and ease of implementation. however, in many cases, we can only get imprecise observation values and the assumptions upon which the least squares is based may not be valid. so this paper characterizes the imprecise data in terms of uncertain variables and proposes a novel robust approach under the principle of least absolute deviations to estimate the unknown parameters in uncertain regression models. finally, numerical examples are documented to illustrate our method.

training competitive binary neural networks from scratch

convolutional neural networks have achieved astonishing results in different application areas. various methods that allow us to use these models on mobile and embedded devices have been proposed. especially binary neural networks are a promising approach for devices with low computational power. however, training accurate binary models from scratch remains a challenge. previous work often uses prior knowledge from full-precision models and complex training strategies. in our work, we focus on increasing the performance of binary neural networks without such prior knowledge and a much simpler training strategy. in our experiments we show that we are able to achieve state-of-the-art results on standard benchmark datasets. further, to the best of our knowledge, we are the first to successfully adopt a network architecture with dense connections for binary networks, which lets us improve the state-of-the-art even further.

modeling urban taxi services with e-hailings: a queueing network approach

the rise of e-hailing taxis has significantly altered urban transportation and resulted in a competitive taxi market with both traditional street-hailing and e-hailing taxis. the new mobility services provide similar door-to-door rides as the traditional one and there is competition across these various services. in this study, we propose an innovative queueing network model for the competitive taxi market and capture the interactions not only within the taxi market but also between the taxi market and urban road system. an example is designed based on data from new york city. numerical results show that the proposed modeling structure, together with the corresponding stationary limits, can capture dynamics within high demand areas using multiple data sources. overall, this study shows how the queueing network approach can measure both the taxi and urban road system performance at an aggregate level. the model can be used to estimate not only the waiting/searching time during passenger-vehicle matching but also the delays in the urban road network. furthermore, the model can be generalized to study the control and management of taxi markets.

the effects of negative adaptation in model-agnostic meta-learning

the capacity of meta-learning algorithms to quickly adapt to a variety of tasks, including ones they did not experience during meta-training, has been a key factor in the recent success of these methods on few-shot learning problems. this particular advantage of using meta-learning over standard supervised or reinforcement learning is only well founded under the assumption that the adaptation phase does improve the performance of our model on the task of interest. however, in the classical framework of meta-learning, this constraint is only mildly enforced, if not at all, and we only see an improvement on average over a distribution of tasks. in this paper, we show that the adaptation in an algorithm like maml can significantly decrease the performance of an agent in a meta-reinforcement learning setting, even on a range of meta-training tasks.

towards a definition of disentangled representations

how can intelligent agents solve a diverse set of tasks in a data-efficient manner? the disentangled representation learning approach posits that such an agent would benefit from separating out (disentangling) the underlying structure of the world into disjoint parts of its representation. however, there is no generally agreed-upon definition of disentangling, not least because it is unclear how to formalise the notion of world structure beyond toy datasets with a known ground truth generative process. here we propose that a principled solution to characterising disentangled representations can be found by focusing on the transformation properties of the world. in particular, we suggest that those transformations that change only some properties of the underlying world state, while leaving all other properties invariant, are what gives exploitable structure to any kind of data. similar ideas have already been successfully applied in physics, where the study of symmetry transformations has revolutionised the understanding of the world structure. by connecting symmetry transformations to vector representations using the formalism of group and representation theory we arrive at the first formal definition of disentangled representations. our new definition is in agreement with many of the current intuitions about disentangling, while also providing principled resolutions to a number of previous points of contention. while this work focuses on formally defining disentangling - as opposed to solving the learning problem - we believe that the shift in perspective to studying data transformations can stimulate the development of better representation learning algorithms.

efficient and robust machine learning for real-world systems

while machine learning is traditionally a resource intensive task, embedded systems, autonomous navigation and the vision of the internet-of-things fuel the interest in resource efficient approaches. these approaches require a carefully chosen trade-off between performance and resource consumption in terms of computation and energy. on top of this, it is crucial to treat uncertainty in a consistent manner in all but the simplest applications of machine learning systems. in particular, a desideratum for any real-world system is to be robust in the presence of outliers and corrupted data, as well as being `aware' of its limits, i.e.\ the system should maintain and provide an uncertainty estimate over its own predictions. these complex demands are among the major challenges in current machine learning research and key to ensure a smooth transition of machine learning technology into every day's applications. in this article, we provide an overview of the current state of the art of machine learning techniques facilitating these real-world requirements. first we provide a comprehensive review of resource-efficiency in deep neural networks with focus on techniques for model size reduction, compression and reduced precision. these techniques can be applied during training or as post-processing and are widely used to reduce both computational complexity and memory footprint. as most (practical) neural networks are limited in their ways to treat uncertainty, we contrast them with probabilistic graphical models, which readily serve these desiderata by means of probabilistic inference. in that way, we provide an extensive overview of the current state-of-the-art of robust and efficient machine learning for real-world systems.

relative entropy regularized policy iteration

we present an off-policy actor-critic algorithm for reinforcement learning (rl) that combines ideas from gradient-free optimization via stochastic search with learned action-value function. the result is a simple procedure consisting of three steps: i) policy evaluation by estimating a parametric action-value function; ii) policy improvement via the estimation of a local non-parametric policy; and iii) generalization by fitting a parametric policy. each step can be implemented in different ways, giving rise to several algorithm variants. our algorithm draws on connections to existing literature on black-box optimization and 'rl as an inference' and it can be seen either as an extension of the maximum a posteriori policy optimisation algorithm (mpo) [abdolmaleki et al., 2018a], or as an extension of trust region covariance matrix adaptation evolutionary strategy (cma-es) [abdolmaleki et al., 2017b; hansen et al., 1997] to a policy iteration scheme. our comparison on 31 continuous control tasks from parkour suite [heess et al., 2017], deepmind control suite [tassa et al., 2018] and openai gym [brockman et al., 2016] with diverse properties, limited amount of compute and a single set of hyperparameters, demonstrate the effectiveness of our method and the state of art results. videos, summarizing results, can be found at goo.gl/htvjkr .

gadget svm: a gossip-based sub-gradient solver for linear svms

in the era of big data, an important weapon in a machine learning researcher's arsenal is a scalable support vector machine (svm) algorithm. svms are extensively used for solving classification problems. traditional algorithms for learning svms often scale super linearly with training set size which becomes infeasible very quickly for large data sets. in recent years, scalable algorithms have been designed which study the primal or dual formulations of the problem. this often suggests a way to decompose the problem and facilitate development of distributed algorithms. in this paper, we present a distributed algorithm for learning linear support vector machines in the primal form for binary classification called gossip-based sub-gradient (gadget) svm. the algorithm is designed such that it can be executed locally on nodes of a distributed system. each node processes its local homogeneously partitioned data and learns a primal svm model. it then gossips with random neighbors about the classifier learnt and uses this information to update the model. extensive theoretical and empirical results suggest that this anytime algorithm has performance comparable to its centralized and online counterparts.

generalizability of predictive models for intensive care unit patients

a large volume of research has considered the creation of predictive models for clinical data; however, much existing literature reports results using only a single source of data. in this work, we evaluate the performance of models trained on the publicly-available eicu collaborative research database. we show that cross-validation using many distinct centers provides a reasonable estimate of model performance in new centers. we further show that a single model trained across centers transfers well to distinct hospitals, even compared to a model retrained using hospital-specific data. our results motivate the use of multi-center datasets for model development and highlight the need for data sharing among hospitals to maximize model performance.

adversarially learned anomaly detection

anomaly detection is a significant and hence well-studied problem. however, developing effective anomaly detection methods for complex and high-dimensional data remains a challenge. as generative adversarial networks (gans) are able to model the complex high-dimensional distributions of real-world data, they offer a promising approach to address this challenge. in this work, we propose an anomaly detection method, adversarially learned anomaly detection (alad) based on bi-directional gans, that derives adversarially learned features for the anomaly detection task. alad then uses reconstruction errors based on these adversarially learned features to determine if a data sample is anomalous. alad builds on recent advances to ensure data-space and latent-space cycle-consistencies and stabilize gan training, which results in significantly improved anomaly detection performance. alad achieves state-of-the-art performance on a range of image and tabular datasets while being several hundred-fold faster at test time than the only published gan-based method.

learning dynamic embeddings from temporal interactions

modeling a sequence of interactions between users and items (e.g., products, posts, or courses) is crucial in domains such as e-commerce, social networking, and education to predict future interactions. representation learning presents an attractive solution to model the dynamic evolution of user and item properties, where each user/item can be embedded in a euclidean space and its evolution can be modeled by dynamic changes in embedding. however, existing embedding methods either generate static embeddings, treat users and items independently, or are not scalable. here we present jodie, a coupled recurrent model to jointly learn the dynamic embeddings of users and items from a sequence of user-item interactions. jodie has three components. first, the update component updates the user and item embedding from each interaction using their previous embeddings with the two mutually-recursive recurrent neural networks. second, a novel projection component is trained to forecast the embedding of users at any future time. finally, the prediction component directly predicts the embedding of the item in a future interaction. for models that learn from a sequence of interactions, traditional training data batching cannot be done due to complex user-user dependencies. therefore, we present a novel batching algorithm called t-batch that generates time-consistent batches of training data that can run in parallel, giving massive speed-up. we conduct six experiments on two prediction tasks---future interaction prediction and state change prediction---using four real-world datasets. we show that jodie outperforms six state-of-the-art algorithms in these tasks by up to 22.4%. moreover, we show that jodie is highly scalable and up to 9.2x faster than comparable models. as an additional experiment, we illustrate that jodie can predict student drop-out from courses five interactions in advance.

on the inductive bias of word-character-level multi-task learning for speech recognition

end-to-end automatic speech recognition (asr) commonly transcribes audio signals into sequences of characters while its performance is evaluated by measuring the word-error rate (wer). this suggests that predicting sequences of words directly may be helpful instead. however, training with word-level supervision can be more difficult due to the sparsity of examples per label class. in this paper we analyze an end-to-end asr model that combines a word-and-character representation in a multi-task learning (mtl) framework. we show that it improves on the wer and study how the word-level model can benefit from character-level supervision by analyzing the learned inductive preference bias of each model component empirically. we find that by adding character-level supervision, the mtl model interpolates between recognizing more frequent words (preferred by the word-level model) and shorter words (preferred by the character-level model).

the mesh-gram neural network model: extending word embedding vectors with mesh concepts for umls semantic similarity and relatedness in the biomedical domain

eliciting semantic similarity between concepts in the biomedical domain remains a challenging task. recent approaches founded on embedding vectors have gained in popularity as they risen to efficiently capture semantic relationships the underlying idea is that two words that have close meaning gather similar contexts. in this study, we propose a new neural network model named mesh-gram which relies on a straighforward approach that extends the skip-gram neural network model by considering mesh (medical subject headings) descriptors instead words. trained on publicly available corpus pubmed medline, mesh-gram is evaluated on reference standards manually annotated for semantic similarity. mesh-gram is first compared to skip-gram with vectors of size 300 and at several windows contexts. a deeper comparison is performed with tewenty existing models. all the obtained results of spearman's rank correlations between human scores and computed similarities show that mesh-gram outperforms the skip-gram model, and is comparable to the best methods but that need more computation and external resources.

a case study : influence of dimension reduction on regression trees-based algorithms -predicting aeronautics loads of a derivative aircraft

in aircraft industry, market needs evolve quickly in a high competitiveness context. this requires adapting a given aircraft model in minimum time considering for example an increase of range or the number of passengers (cf a330 neo family). the computation of loads and stress to resize the airframe is on the critical path of this aircraft variant definition: this is a consuming and costly process, one of the reason being the high dimen-sionality and the large amount of data. this is why airbus has invested since a couple of years in big data approaches (statistic methods up to machine learning) to improve the speed, the data value extraction and the responsiveness of this process. this paper presents recent advances in this work made in cooperation between airbus, enac and institut de math{\'e}-matiques de toulouse in the framework of a proof of value sprint project. it compares the influence of three dimensional reduction techniques (pca, polynomial fitting, combined) on the extrapolation capabilities of regression trees based algorithms for loads prediction. it shows that adaboost with random forest offers promising results in average in terms of accuracy and computational time to estimate loads on which a pca is applied only on the outputs.

multiple manifold clustering using curvature constrained path

the problem of multiple surface clustering is a challenging task, particularly when the surfaces intersect. available methods such as isomap fail to capture the true shape of the surface nearby the intersection and result in incorrect clustering. the isomap algorithm uses the shortest path between points. the main draw back of the shortest path algorithm is due to the lack of curvature constrained where causes to have a path between points on different surfaces. in this paper, we tackle this problem by imposing a curvature constraint to the shortest path algorithm used in isomap. the algorithm chooses several landmark nodes at random and then checks whether there is a curvature constrained path between each landmark node and every other node in the neighbourhood graph. we build a binary feature vector for each point where each entry represents the connectivity of that point to a particular landmark. then the binary feature vectors could be used as an input of conventional clustering algorithm such as hierarchical clustering. we apply our method to simulated and some real datasets and show, it performs comparably to the best methods such as k-manifold and spectral multi-manifold clustering.

time-discounting convolution for event sequences with ambiguous timestamps

this paper proposes a method for modeling event sequences with ambiguous timestamps, a time-discounting convolution. unlike in ordinary time series, time intervals are not constant, small time-shifts have no significant effect, and inputting timestamps or time durations into a model is not effective. the criteria that we require for the modeling are providing robustness against time-shifts or timestamps uncertainty as well as maintaining the essential capabilities of time-series models, i.e., forgetting meaningless past information and handling infinite sequences. the proposed method handles them with a convolutional mechanism across time with specific parameterizations, which efficiently represents the event dependencies in a time-shift invariant manner while discounting the effect of past events, and a dynamic pooling mechanism, which provides robustness against the uncertainty in timestamps and enhances the time-discounting capability by dynamically changing the pooling window size. in our learning algorithm, the decaying and dynamic pooling mechanisms play critical roles in handling infinite and variable length sequences. numerical experiments on real-world event sequences with ambiguous timestamps and ordinary time series demonstrated the advantages of our method.

elastic gossip: distributing neural network training using gossip-like protocols

distributing neural network training is of particular interest for several reasons including scaling using computing clusters, training at data sources such as iot devices and edge servers, utilizing underutilized resources across heterogeneous environments, and so on. most contemporary approaches primarily address scaling using computing clusters and require high network bandwidth and frequent communication. this thesis presents an overview of standard approaches to distribute training and proposes a novel technique involving pairwise-communication using gossip-like protocols, called elastic gossip. this approach builds upon an existing technique known as elastic averaging sgd (easgd), and is similar to another technique called gossiping sgd which also uses gossip-like protocols. elastic gossip is empirically evaluated against gossiping sgd using the mnist digit recognition and cifar-10 classification tasks, using commonly used neural network architectures spanning multi-layer perceptrons (mlps) and convolutional neural networks (cnns). it is found that elastic gossip, gossiping sgd, and all-reduce sgd perform quite comparably, even though the latter entails a substantially higher communication cost. while elastic gossip performs better than gossiping sgd in these experiments, it is possible that a more thorough search over hyper-parameter space, specific to a given application, may yield configurations of gossiping sgd that work better than elastic gossip.

goodness-of-fit testing the error distribution in multivariate indirect regression

we propose a goodness-of-fit test for the distribution of errors from a multivariate indirect regression model. the test statistic is based on the khmaladze transformation of the empirical process of standardized residuals. this goodness-of-fit test is consistent at the root-n rate of convergence, and the test can maintain power against local alternatives converging to the null at a root-n rate.

using published bid/ask curves to error dress spot electricity price forecasts

accurate forecasts of electricity spot prices are essential to the daily operational and planning decisions made by power producers and distributors. typically, point forecasts of these quantities suffice, particularly in the nord pool market where the large quantity of hydro power leads to price stability. however, when situations become irregular, deviations on the price scale can often be extreme and difficult to pinpoint precisely, which is a result of the highly varying marginal costs of generating facilities at the edges of the load curve. in these situations it is useful to supplant a point forecast of price with a distributional forecast, in particular one whose tails are adaptive to the current production regime. this work outlines a methodology for leveraging published bid/ask information from the nord pool market to construct such adaptive predictive distributions. our methodology is a non-standard application of the concept of error-dressing, which couples a feature driven error distribution in volume space with a non-linear transformation via the published bid/ask curves to obtain highly non-symmetric, adaptive price distributions. using data from the nord pool market, we show that our method outperforms more standard forms of distributional modeling. we further show how such distributions can be used to render `warning systems' that issue reliable probabilities of prices exceeding various important thresholds.

anomaly detection with wasserstein gan

generative adversarial networks are a class of generative algorithms that have been widely used to produce state-of-the-art samples. in this paper, we investigate gan to perform anomaly detection on time series dataset. in order to achieve this goal, a bibliography is made focusing on theoretical properties of gan and gan used for anomaly detection. a wasserstein gan has been chosen to learn the representation of normal data distribution and a stacked encoder with the generator performs the anomaly detection. w-gan with encoder seems to produce state of the art anomaly detection scores on mnist dataset and we investigate its usage on multi-variate time series.

active learning methods based on statistical leverage scores

in many real-world machine learning applications, unlabeled data are abundant whereas class labels are expensive and scarce. an active learner aims to obtain a model of high accuracy with as few labeled instances as possible by effectively selecting useful examples for labeling. we propose a new selection criterion that is based on statistical leverage scores and present two novel active learning methods based on this criterion: alevs for querying single example at each iteration and dbalevs for querying a batch of examples. to assess the representativeness of the examples in the pool, alevs and dbalevs use the statistical leverage scores of the kernel matrices computed on the examples of each class. additionally, dbalevs selects a diverse a set of examples that are highly representative but are dissimilar to already labeled examples through maximizing a submodular set function defined with the statistical leverage scores and the kernel matrix computed on the pool of the examples. the submodularity property of the set scoring function let us identify batches with a constant factor approximate to the optimal batch in an efficient manner. our experiments on diverse datasets show that querying based on leverage scores is a powerful strategy for active learning.

a novel health risk model based on intraday physical activity time series collected by smartphones

we compiled a demo application and collected a motion database of more than 10,000 smartphone users to produce a health risk model trained on physical activity streams. we turned to adversarial domain adaptation and employed the uk biobank dataset of motion data, augmented by a rich set of clinical information as the source domain to train the model using a deep residual convolutional neuron network (resnet). the model risk score is a biomarker of ageing, since it was predictive of lifespan and healthspan (as defined by the onset of specified diseases), and was elevated in groups associated with life-shortening lifestyles, such as smoking. we ascertained the target domain performance in a smaller cohort of the mobile application that included users who were willing to share answers to a short questionnaire related to their disease and smoking status. we thus conclude that the proposed pipeline combining deep convolutional and domain adversarial neuron networks (dann) is a powerful tool for disease risk and lifestyle-associated hazard assessment from mobile motion sensors that are transferable across devices and populations.

energy efficiency in reinforcement learning for wireless sensor networks

as sensor networks for health monitoring become more prevalent, so will the need to control their usage and consumption of energy. this paper presents a method which leverages the algorithm's performance and energy consumption. by utilising reinforcement learning (rl) techniques, we provide an adaptive framework, which continuously performs weak training in an energy-aware system. we motivate this using a realistic example of residential localisation based on received signal strength (rss). the method is cheap in terms of work-hours, calibration and energy usage. it achieves this by utilising other sensors available in the environment. these other sensors provide weak labels, which are then used to employ the state-action-reward-state-action (sarsa) algorithm and train the model over time. our approach is evaluated on a simulated localisation environment and validated on a widely available pervasive health dataset which facilitates realistic residential localisation using rss. we show that our method is cheaper to implement and requires less effort, whilst at the same time providing a performance enhancement and energy savings over time.

a two-stage hybrid model by using artificial neural networks as feature construction algorithms

we propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. the hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional input variables in logistic regression in the second stage. the model is compared with the traditional one-stage model in credit customer response classification. it is observed that the proposed two-stage model outperforms the one-stage model in terms of accuracy, the area under roc curve, and ks statistic. by creating new features with the neural network technique, the underlying nonlinear relationships between variables are identified. furthermore, by using a very simple neural network structure, the model could overcome the drawbacks of neural networks in terms of its long training time, complex topology, and limited interpretability.

prior networks for detection of adversarial attacks

adversarial examples are considered a serious issue for safety critical applications of ai, such as finance, autonomous vehicle control and medicinal applications. though significant work has resulted in increased robustness of systems to these attacks, systems are still vulnerable to well-crafted attacks. to address this problem, several adversarial attack detection methods have been proposed. however, a system can still be vulnerable to adversarial samples that are designed to specifically evade these detection methods. one recent detection scheme that has shown good performance is based on uncertainty estimates derived from monte-carlo dropout ensembles. prior networks, a new method of estimating predictive uncertainty, has been shown to outperform monte-carlo dropout on a range of tasks. one of the advantages of this approach is that the behaviour of a prior network can be explicitly tuned to, for example, predict high uncertainty in regions where there are no training data samples. in this work, prior networks are applied to adversarial attack detection using measures of uncertainty in a similar fashion to monte-carlo dropout. detection based on measures of uncertainty derived from dnns and monte-carlo dropout ensembles are used as a baseline. prior networks are shown to significantly out-perform these baseline approaches over a range of adversarial attacks in both detection of whitebox and blackbox configurations. even when the adversarial attacks are constructed with full knowledge of the detection mechanism, it is shown to be highly challenging to successfully generate an adversarial sample.

finding the needle in high-dimensional haystack: a tutorial on canonical correlation analysis

since the beginning of the 21st century, the size, breadth, and granularity of data in biology and medicine has grown rapidly. in the example of neuroscience, studies with thousands of subjects are becoming more common, which provide extensive phenotyping on the behavioral, neural, and genomic level with hundreds of variables. the complexity of such big data repositories offer new opportunities and pose new challenges to investigate brain, cognition, and disease. canonical correlation analysis (cca) is a prototypical family of methods for wrestling with and harvesting insight from such rich datasets. this doubly-multivariate tool can simultaneously consider two variable sets from different modalities to uncover essential hidden associations. our primer discusses the rationale, promises, and pitfalls of cca in biomedicine.

automatic hyperparameter selection in autodock

autodock is a widely used molecular modeling tool which predicts how small molecules bind to a receptor of known 3d structure. the current version of autodock uses meta-heuristic algorithms in combination with local search methods for doing the conformation search. appropriate settings of hyperparameters in these algorithms are important, particularly for novice users who often find it hard to identify the best configuration. in this work, we design a surrogate based multi-objective algorithm to help such users by automatically tuning hyperparameter settings. the proposed method iteratively uses a radial basis function model and non-dominated sorting to evaluate the sampled configurations during the search phase. our experimental results using autodock show that the introduced component is practical and effective.

convolutional neural networks with transformed input based on robust tensor network decomposition

tensor network decomposition, originated from quantum physics to model entangled many-particle quantum systems, turns out to be a promising mathematical technique to efficiently represent and process big data in parsimonious manner. in this study, we show that tensor networks can systematically partition structured data, e.g. color images, for distributed storage and communication in privacy-preserving manner. leveraging the sea of big data and metadata privacy, empirical results show that neighbouring subtensors with implicit information stored in tensor network formats cannot be identified for data reconstruction. this technique complements the existing encryption and randomization techniques which store explicit data representation at one place and highly susceptible to adversarial attacks such as side-channel attacks and de-anonymization. furthermore, we propose a theory for adversarial examples that mislead convolutional neural networks to misclassification using subspace analysis based on singular value decomposition (svd). the theory is extended to analyze higher-order tensors using tensor-train svd (tt-svd); it helps to explain the level of susceptibility of different datasets to adversarial attacks, the structural similarity of different adversarial attacks including global and localized attacks, and the efficacy of different adversarial defenses based on input transformation. an efficient and adaptive algorithm based on robust tt-svd is then developed to detect strong and static adversarial attacks.

$\beta$-vaes can retain label information even at high compression

in this paper, we investigate the degree to which the encoding of a $\beta$-vae captures label information across multiple architectures on binary static mnist and omniglot. even though they are trained in a completely unsupervised manner, we demonstrate that a $\beta$-vae can retain a large amount of label information, even when asked to learn a highly compressed representation.

adaptive multicenter designs for continuous response clinical trials in the presence of an unknown sensitive subgroup

the partial effectiveness of drugs is of importance to the pharmaceutical industry. randomized controlled trials (rcts) assuming the existence of a subgroup sensitive to the treatment are already used. these designs, however, are available only if there is a known marker for identifying subjects in the subgroup. in this paper we investigate a model in which the response in the treatment group $z^t$ has a two-component mixture density $(1-p)\mathcal n(\mu^c, \sigma^2)+p\mathcal n(\mu^t, \sigma^2)$ representing the treatment responses of \emph{placebo responders} and \emph{drug responders}. the treatment-specific effect is $\mu = \frac{\mu^t-\mu^c}{\sigma}$ and $p$ is the prevalence of the drug responders in the population. other patients in the treatment group react as if they had received a placebo. we develop one- and two-stage rct designs that are able to detect a sensitive subgroup based solely on the responses. we also extend them to a multicenter rcts using hochberg's step-up procedure. we avoid extensive simulations and use simple and quick numerical optimization methods.

higher-order stein kernels for gaussian approximation

we introduce higher-order stein kernels relative to the standard gaussian measure, which generalize the usual stein kernels by involving higher-order derivatives of test functions. we relate the associated discrepancies to various metrics on the space of probability measures and prove new functional inequalities involving them. as an application, we obtain new explicit improved rates of convergence in the classical multidimensional clt under higher moment and regularity assumptions.

visual object networks: image generation with disentangled 3d representation

recent progress in deep generative models has led to tremendous breakthroughs in image generation. however, while existing models can synthesize photorealistic images, they lack an understanding of our underlying 3d world. we present a new generative model, visual object networks (von), synthesizing natural images of objects with a disentangled 3d representation. inspired by classic graphics rendering pipelines, we unravel our image formation process into three conditionally independent factors---shape, viewpoint, and texture---and present an end-to-end adversarial learning framework that jointly models 3d shapes and 2d images. our model first learns to synthesize 3d shapes that are indistinguishable from real shapes. it then renders the object's 2.5d sketches (i.e., silhouette and depth map) from its shape under a sampled viewpoint. finally, it learns to add realistic texture to these 2.5d sketches to generate natural images. the von not only generates images that are more realistic than state-of-the-art 2d image synthesis methods, but also enables many 3d operations such as changing the viewpoint of a generated image, editing of shape and texture, linear interpolation in texture and shape space, and transferring appearance across different objects and viewpoints.

a technical survey on statistical modelling and design methods for crowdsourcing quality control

online crowdsourcing provides a scalable and inexpensive means to collect knowledge (e.g. labels) about various types of data items (e.g. text, audio, video). however, it is also known to result in large variance in the quality of recorded responses which often cannot be directly used for training machine learning systems. to resolve this issue, a lot of work has been conducted to control the response quality such that low-quality responses cannot adversely affect the performance of the machine learning systems. such work is referred to as the quality control for crowdsourcing. past quality control research can be divided into two major branches: quality control mechanism design and statistical models. the first branch focuses on designing measures, thresholds, interfaces and workflows for payment, gamification, question assignment and other mechanisms that influence workers' behaviour. the second branch focuses on developing statistical models to perform effective aggregation of responses to infer correct responses. the two branches are connected as statistical models (i) provide parameter estimates to support the measure and threshold calculation, and (ii) encode modelling assumptions used to derive (theoretical) performance guarantees for the mechanisms. there are surveys regarding each branch but they lack technical details about the other branch. our survey is the first to bridge the two branches by providing technical details on how they work together under frameworks that systematically unify crowdsourcing aspects modelled by both of them to determine the response quality. we are also the first to provide taxonomies of quality control papers based on the proposed frameworks. finally, we specify the current limitations and the corresponding future directions for the quality control research.

improving reconstruction autoencoder out-of-distribution detection with mahalanobis distance

there is an increasingly apparent need for validating the classifications made by deep learning systems in safety-critical applications like autonomous vehicle systems. a number of recent papers have proposed methods for detecting anomalous image data that appear different from known inlier data samples, including reconstruction-based autoencoders. autoencoders optimize the compression of input data to a latent space of a dimensionality smaller than the original input and attempt to accurately reconstruct the input using that compressed representation. since the latent vector is optimized to capture the salient features from the inlier class only, it is commonly assumed that images of objects from outside of the training class cannot effectively be compressed and reconstructed. some thus consider reconstruction error as a kind of novelty measure. here we suggest that reconstruction-based approaches fail to capture particular anomalies that lie far from known inlier samples in latent space but near the latent dimension manifold defined by the parameters of the model. we propose incorporating the mahalanobis distance in latent space to better capture these out-of-distribution samples and our results show that this method often improves performance over the baseline approach.

squeezefit: label-aware dimensionality reduction by semidefinite programming

given labeled points in a high-dimensional vector space, we seek a low-dimensional subspace such that projecting onto this subspace maintains some prescribed distance between points of differing labels. intended applications include compressive classification. taking inspiration from large margin nearest neighbor classification, this paper introduces a semidefinite relaxation of this problem. unlike its predecessors, this relaxation is amenable to theoretical analysis, allowing us to provably recover a planted projection operator from the data.

embedding-reparameterization procedure for manifold-valued latent variables in generative models

conventional prior for variational auto-encoder (vae) is a gaussian distribution. recent works demonstrated that choice of prior distribution affects learning capacity of vae models. we propose a general technique (embedding-reparameterization procedure, or er) for introducing arbitrary manifold-valued variables in vae model. we compare our technique with a conventional vae on a toy benchmark problem. this is work in progress.

verification of deep probabilistic models

probabilistic models are a critical part of the modern deep learning toolbox - ranging from generative models (vaes, gans), sequence to sequence models used in machine translation and speech processing to models over functional spaces (conditional neural processes, neural processes). given the size and complexity of these models, safely deploying them in applications requires the development of tools to analyze their behavior rigorously and provide some guarantees that these models are consistent with a list of desirable properties or specifications. for example, a machine translation model should produce semantically equivalent outputs for innocuous changes in the input to the model. a functional regression model that is learning a distribution over monotonic functions should predict a larger value at a larger input. verification of these properties requires a new framework that goes beyond notions of verification studied in deterministic feedforward networks, since requiring worst-case guarantees in probabilistic models is likely to produce conservative or vacuous results. we propose a novel formulation of verification for deep probabilistic models that take in conditioning inputs and sample latent variables in the course of producing an output: we require that the output of the model satisfies a linear constraint with high probability over the sampling of latent variables and for every choice of conditioning input to the model. we show that rigorous lower bounds on the probability that the constraint is satisfied can be obtained efficiently. experiments with neural processes show that several properties of interest while modeling functional spaces can be modeled within this framework (monotonicity, convexity) and verified efficiently using our algorithms