mtybilly/Instruct2DS huggingface dataset:
Multicalibration: Calibration for the (Computationally-Identifiable) Masses
Authors: Hebert-Johnson, Ursula and Kim, Michael and Reingold, Omer and Rothblum, Guy
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We develop and study multicalibration as a new measure of fairness in machine learning that aims to mitigate inadvertent or malicious discrimination that is introduced at training time (even from ground truth data). Multicalibration guarantees meaningful (calibrated) predictions for every subpopulation that can be identified within a specified class of computations. The specified class can be quite rich; in particular, it can contain many overlapping subgroups of a protected group. We demonstrate that in many settings this strong notion of protection from discrimination is provably attainable and aligned with the goal of obtaining accurate predictions. Along the way, we present algorithms for learning a multicalibrated predictor, study the computational complexity of this task, and illustrate tight connections to the agnostic learning model.
Recurrent Predictive State Policy Networks
Authors: Hefny, Ahmed and Marinho, Zita and Sun, Wen and Srinivasa, Siddhartha and Gordon, Geoffrey
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We introduce Recurrent Predictive State Policy(RPSP) networks, a recurrent architecture that brings insights from predictive state representations to reinforcement learning in partially ob-servable environments. Predictive state policy networks consist of a recursive filter, which keeps track of a belief about the state of the environment, and a reactive policy that directly maps beliefs to actions, to maximize the cumulative reward. The recursive filter leverages predictive state representations (PSRs) (Rosencrantz & Gordon, 2004; Sun et al., 2016) by modeling predictive state—a prediction of the distribution of future observations conditioned on history and future actions.This representation gives rise to a rich class of statistically consistent algorithms (Hefny et al.,2017) to initialize the recursive filter. Predictive stats serves as an equivalent representation of a belief state. Therefore, the policy component of the RPSP-network can be purely reactive, simplifying training while still allowing optimal behavior. Moreover, we use the PSR interpretation during training as well, by incorporating prediction error in the loss function. The entire network (recursive filter and reactive policy) is still differentiable and can be trained using gradient-based methods. We optimize our policy using a combination of policy gradient based on rewards (Williams, 1992)and gradient descent based on prediction error.We show the efficacy of RPSP-networks on a set of robotic control tasks from OpenAI Gym. We empirically show that RPSP-networks perform well compared with memory-preserving networks such as GRUs, as well as finite memory models, being the overall best performing method.
Learning unknown ODE models with Gaussian processes
Authors: Heinonen, Markus and Yildiz, Cagatay and Mannerström, Henrik and Intosalmi, Jukka and Lähdesmäki, Harri
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In conventional ODE modelling coefficients of an equation driving the system state forward in time are estimated. However, for many complex systems it is practically impossible to determine the equations or interactions governing the underlying dynamics. In these settings, parametric ODE model cannot be formulated. Here, we overcome this issue by introducing a novel paradigm of nonparametric ODE modelling that can learn the underlying dynamics of arbitrary continuous-time systems without prior knowledge. We propose to learn non-linear, unknown differential functions from state observations using Gaussian process vector fields within the exact ODE formalism. We demonstrate the model’s capabilities to infer dynamics from sparse data and to simulate the system forward into future.
Orthogonal Recurrent Neural Networks with Scaled Cayley Transform
Authors: Helfrich, Kyle and Willmott, Devin and Ye, Qiang
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Recurrent Neural Networks (RNNs) are designed to handle sequential data but suffer from vanishing or exploding gradients. Recent work on Unitary Recurrent Neural Networks (uRNNs) have been used to address this issue and in some cases, exceed the capabilities of Long Short-Term Memory networks (LSTMs). We propose a simpler and novel update scheme to maintain orthogonal recurrent weight matrices without using complex valued matrices. This is done by parametrizing with a skew-symmetric matrix using the Cayley transform; such a parametrization is unable to represent matrices with negative one eigenvalues, but this limitation is overcome by scaling the recurrent weight matrix by a diagonal matrix consisting of ones and negative ones. The proposed training scheme involves a straightforward gradient calculation and update step. In several experiments, the proposed scaled Cayley orthogonal recurrent neural network (scoRNN) achieves superior results with fewer trainable parameters than other unitary RNNs.
Fast Bellman Updates for Robust MDPs
Authors: Ho, Chin Pang and Petrik, Marek and Wiesemann, Wolfram
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We describe two efficient, and exact, algorithms for computing Bellman updates in robust Markov decision processes (MDPs). The first algorithm uses a homotopy continuation method to compute updates for L1-constrained s,a-rectangular ambiguity sets. It runs in quasi-linear time for plain L1-norms and also generalizes to weighted L1-norms. The second algorithm uses bisection to compute updates for robust MDPs with s-rectangular ambiguity sets. This algorithm, when combined with the homotopy method, also has a quasi-linear runtime. Unlike previous methods, our algorithms compute the primal solution in addition to the optimal objective value, which makes them useful in policy iteration methods. Our experimental results indicate that the proposed methods are over 1,000 times faster than Gurobi, a state-of-the-art commercial optimization package, for small instances, and the performance gap grows considerably with problem size.
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Authors: Hoffman, Judy and Tzeng, Eric and Park, Taesung and Zhu, Jun-Yan and Isola, Phillip and Saenko, Kate and Efros, Alexei and Darrell, Trevor
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models have shown tremendous progress towards adapting to new environments by focusing either on discovering domain invariant representations or by mapping between unpaired image domains. While feature space methods are difficult to interpret and sometimes fail to capture pixel-level and low-level domain shifts, image space methods sometimes fail to incorporate high level semantic knowledge relevant for the end task. We propose a model which adapts between domains using both generative image space alignment and latent representation space alignment. Our approach, Cycle-Consistent Adversarial Domain Adaptation (CyCADA), guides transfer between domains according to a specific discriminatively trained task and avoids divergence by enforcing consistency of the relevant semantics before and after adaptation. We evaluate our method on a variety of visual recognition and prediction settings, including digit classification and semantic segmentation of road scenes, advancing state-of-the-art performance for unsupervised adaptation from synthetic to real world driving domains.
Sound Abstraction and Decomposition of Probabilistic Programs
Authors: Holtzen, Steven and Van den Broeck, Guy and Millstein, Todd
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Probabilistic programming languages are a flexible tool for specifying statistical models, but this flexibility comes at the cost of efficient analysis. It is currently difficult to compactly represent the subtle independence properties of a probabilistic program, and exploit independence properties to decompose inference. Classical graphical model abstractions do capture some properties of the underlying distribution, enabling inference algorithms to operate at the level of the graph topology. However, we observe that graph-based abstractions are often too coarse to capture interesting properties of programs. We propose a form of sound abstraction for probabilistic programs wherein the abstractions are themselves simplified programs. We provide a theoretical foundation for these abstractions, as well as an algorithm to generate them. Experimentally, we also illustrate the practical benefits of our framework as a tool to decompose probabilistic program inference.
Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks
Authors: Hong, Mingyi and Razaviyayn, Meisam and Lee, Jason
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In this work, we study two first-order primal-dual based algorithms, the Gradient Primal-Dual Algorithm (GPDA) and the Gradient Alternating Direction Method of Multipliers (GADMM), for solving a class of linearly constrained non-convex optimization problems. We show that with random initialization of the primal and dual variables, both algorithms are able to compute second-order stationary solutions (ss2) with probability one. This is the first result showing that primal-dual algorithm is capable of finding ss2 when only using first-order information; it also extends the existing results for first-order, but primal-only algorithms. An important implication of our result is that it also gives rise to the first global convergence result to the ss2, for two classes of unconstrained distributed non-convex learning problems over multi-agent networks.
Variational Bayesian dropout: pitfalls and fixes
Authors: Hron, Jiri and Matthews, Alex and Ghahramani, Zoubin
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Dropout, a stochastic regularisation technique for training of neural networks, has recently been reinterpreted as a specific type of approximate inference algorithm for Bayesian neural networks. The main contribution of the reinterpretation is in providing a theoretical framework useful for analysing and extending the algorithm. We show that the proposed framework suffers from several issues; from undefined or pathological behaviour of the true posterior related to use of improper priors, to an ill-defined variational objective due to singularity of the approximating distribution relative to the true posterior. Our analysis of the improper log uniform prior used in variational Gaussian dropout suggests the pathologies are generally irredeemable, and that the algorithm still works only because the variational formulation annuls some of the pathologies. To address the singularity issue, we proffer Quasi-KL (QKL) divergence, a new approximate inference objective for approximation of high-dimensional distributions. We show that motivations for variational Bernoulli dropout based on discretisation and noise have QKL as a limit. Properties of QKL are studied both theoretically and on a simple practical example which shows that the QKL-optimal approximation of a full rank Gaussian with a degenerate one naturally leads to the Principal Component Analysis solution.
Does Distributionally Robust Supervised Learning Give Robust Classifiers?
Authors: Hu, Weihua and Niu, Gang and Sato, Issei and Sugiyama, Masashi
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Distributionally Robust Supervised Learning (DRSL) is necessary for building reliable machine learning systems. When machine learning is deployed in the real world, its performance can be significantly degraded because test data may follow a different distribution from training data. DRSL with f-divergences explicitly considers the worst-case distribution shift by minimizing the adversarially reweighted training loss. In this paper, we analyze this DRSL, focusing on the classification scenario. Since the DRSL is explicitly formulated for a distribution shift scenario, we naturally expect it to give a robust classifier that can aggressively handle shifted distributions. However, surprisingly, we prove that the DRSL just ends up giving a classifier that exactly fits the given training distribution, which is too pessimistic. This pessimism comes from two sources: the particular losses used in classification and the fact that the variety of distributions to which the DRSL tries to be robust is too wide. Motivated by our analysis, we propose simple DRSL that overcomes this pessimism and empirically demonstrate its effectiveness.
Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs
Authors: Hu, Bin and Wright, Stephen and Lessard, Laurent
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Techniques for reducing the variance of gradient estimates used in stochastic programming algorithms for convex finite-sum problems have received a great deal of attention in recent years. By leveraging dissipativity theory from control, we provide a new perspective on two important variance-reduction algorithms: SVRG and its direct accelerated variant Katyusha. Our perspective provides a physically intuitive understanding of the behavior of SVRG-like methods via a principle of energy conservation. The tools discussed here allow us to automate the convergence analysis of SVRG-like methods by capturing their essential properties in small semidefinite programs amenable to standard analysis and computational techniques. Our approach recovers existing convergence results for SVRG and Katyusha and generalizes the theory to alternative parameter choices. We also discuss how our approach complements the linear coupling technique. Our combination of perspectives leads to a better understanding of accelerated variance-reduced stochastic methods for finite-sum problems.
Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices
Authors: Huang, Zengfeng
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Given a large matrix $Aıne̊al^n\times d$, we consider the problem of computing a sketch matrix $Bın\ål^\ell\times d$ which is significantly smaller than but still well approximates $A$. We are interested in minimizing the <em>covariance error</em> $\normA^TA-B^TB_2.$We consider the problems in the streaming model, where the algorithm can only make one pass over the input with limited working space. The popular Frequent Directions algorithm of Liberty (2013) and its variants achieve optimal space-error tradeoff. However, whether the running time can be improved remains an unanswered question.In this paper, we almost settle the time complexity of this problem. In particular, we provide new space-optimal algorithms with faster running times. Moreover, we also show that the running times of our algorithms are near-optimal unless the state-of-the-art running time of matrix multiplication can be improved significantly.
Learning Deep ResNet Blocks Sequentially using Boosting Theory
Authors: Huang, Furong and Ash, Jordan and Langford, John and Schapire, Robert
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We prove a <em>multi-channel telescoping sum boosting</em> theory for the ResNet architectures which simultaneously creates a new technique for boosting over features (in contrast with labels) and provides a new algorithm for ResNet-style architectures. Our proposed training algorithm, <em>BoostResNet</em>, is particularly suitable in non-differentiable architectures. Our method only requires the relatively inexpensive sequential training of $T$ “shallow ResNets”. We prove that the training error decays exponentially with the depth $T$ if the weak module classifiers that we train perform slightly better than some weak baseline. In other words, we propose a weak learning condition and prove a boosting theory for ResNet under the weak learning condition. A generalization error bound based on margin theory is proved and suggests that ResNet could be resistant to overfitting using a network with $l_1$ norm bounded weights.
Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling
Authors: Huang, Kejun and Fu, Xiao and Sidiropoulos, Nicholas
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We present a new algorithm for identifying the transition and emission probabilities of a hidden Markov model (HMM) from the emitted data. Expectation-maximization becomes computationally prohibitive for long observation records, which are often required for identification. The new algorithm is particularly suitable for cases where the available sample size is large enough to accurately estimate second-order output probabilities, but not higher-order ones. We show that if one is only able to obtain a reliable estimate of the pairwise co-occurrence probabilities of the emissions, it is still possible to uniquely identify the HMM if the emission probability is <em>sufficiently scattered</em>. We apply our method to hidden topic Markov modeling, and demonstrate that we can learn topics with higher quality if documents are modeled as observations of HMMs sharing the same emission (topic) probability, compared to the simple but widely used bag-of-words model.
Neural Autoregressive Flows
Authors: Huang, Chin-Wei and Krueger, David and Lacoste, Alexandre and Courville, Aaron
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Normalizing flows and autoregressive models have been successfully combined to produce state-of-the-art results in density estimation, via Masked Autoregressive Flows (MAF) (Papamakarios et al., 2017), and to accelerate state-of-the-art WaveNet-based speech synthesis to 20x faster than real-time (Oord et al., 2017), via Inverse Autoregressive Flows (IAF) (Kingma et al., 2016). We unify and generalize these approaches, replacing the (conditionally) affine univariate transformations of MAF/IAF with a more general class of invertible univariate transformations expressed as monotonic neural networks. We demonstrate that the proposed neural autoregressive flows (NAF) are universal approximators for continuous probability distributions, and their greater expressivity allows them to better capture multimodal target distributions. Experimentally, NAF yields state-of-the-art performance on a suite of density estimation tasks and outperforms IAF in variational autoencoders trained on binarized MNIST.
Topological mixture estimation
Authors: Huntsman, Steve
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We introduce topological mixture estimation, a completely nonparametric and computationally efficient solution to the problem of estimating a one-dimensional mixture with generic unimodal components. We repeatedly perturb the unimodal decomposition of Baryshnikov and Ghrist to produce a topologically and information-theoretically optimal unimodal mixture. We also detail a smoothing process that optimally exploits topological persistence of the unimodal category in a natural way when working directly with sample data. Finally, we illustrate these techniques through examples.
Decoupled Parallel Backpropagation with Convergence Guarantee
Authors: Huo, Zhouyuan and Gu, Bin and qian Yang and Huang, Heng
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Backpropagation algorithm is indispensable for the training of feedforward neural networks. It requires propagating error gradients sequentially from the output layer all the way back to the input layer. The backward locking in backpropagation algorithm constrains us from updating network layers in parallel and fully leveraging the computing resources. Recently, several algorithms have been proposed for breaking the backward locking. However, their performances degrade seriously when networks are deep. In this paper, we propose decoupled parallel backpropagation algorithm for deep learning optimization with convergence guarantee. Firstly, we decouple the backpropagation algorithm using delayed gradients, and show that the backward locking is removed when we split the networks into multiple modules. Then, we utilize decoupled parallel backpropagation in two stochastic methods and prove that our method guarantees convergence to critical points for the non-convex problem. Finally, we perform experiments for training deep convolutional neural networks on benchmark datasets. The experimental results not only confirm our theoretical analysis, but also demonstrate that the proposed method can achieve significant speedup without loss of accuracy.
Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning
Authors: Icarte, Rodrigo Toro and Klassen, Toryn and Valenzano, Richard and McIlraith, Sheila
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In this paper we propose Reward Machines — a type of finite state machine that supports the specification of reward functions while exposing reward function structure to the learner and supporting decomposition. We then present Q-Learning for Reward Machines (QRM), an algorithm which appropriately decomposes the reward machine and uses off-policy q-learning to simultaneously learn subpolicies for the different components. QRM is guaranteed to converge to an optimal policy in the tabular case, in contrast to Hierarchical Reinforcement Learning methods which might converge to suboptimal policies. We demonstrate this behavior experimentally in two discrete domains. We also show how function approximation methods like neural networks can be incorporated into QRM, and that doing so can find better policies more quickly than hierarchical methods in a domain with a continuous state space.
Deep Variational Reinforcement Learning for POMDPs
Authors: Igl, Maximilian and Zintgraf, Luisa and Le, Tuan Anh and Wood, Frank and Whiteson, Shimon
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Many real-world sequential decision making problems are partially observable by nature, and the environment model is typically unknown. Consequently, there is great need for reinforcement learning methods that can tackle such problems given only a stream of rewards and incomplete and noisy observations. In this paper, we propose deep variational reinforcement learning (DVRL), which introduces an inductive bias that allows an agent to learn a generative model of the environment and perform inference in that model to effectively aggregate the available information. We develop an n-step approximation to the evidence lower bound (ELBO), allowing the model to be trained jointly with the policy. This ensures that the latent state representation is suitable for the control task. In experiments on Mountain Hike and flickering Atari we show that our method outperforms previous approaches relying on recurrent neural networks to encode the past.
Attention-based Deep Multiple Instance Learning
Authors: Ilse, Maximilian and Tomczak, Jakub and Welling, Max
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Multiple instance learning (MIL) is a variation of supervised learning where a single class label is assigned to a bag of instances. In this paper, we state the MIL problem as learning the Bernoulli distribution of the bag label where the bag label probability is fully parameterized by neural networks. Furthermore, we propose a neural network-based permutation-invariant aggregation operator that corresponds to the attention mechanism. Notably, an application of the proposed attention-based operator provides insight into the contribution of each instance to the bag label. We show empirically that our approach achieves comparable performance to the best MIL methods on benchmark MIL datasets and it outperforms other methods on a MNIST-based MIL dataset and two real-life histopathology datasets without sacrificing interpretability.
Black-box Adversarial Attacks with Limited Queries and Information
Authors: Ilyas, Andrew and Engstrom, Logan and Athalye, Anish and Lin, Jessy
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Current neural network-based classifiers are susceptible to adversarial examples even in the black-box setting, where the attacker only has query access to the model. In practice, the threat model for real-world systems is often more restrictive than the typical black-box model where the adversary can observe the full output of the network on arbitrarily many chosen inputs. We define three realistic threat models that more accurately characterize many real-world classifiers: the query-limited setting, the partial-information setting, and the label-only setting. We develop new attacks that fool classifiers under these more restrictive threat models, where previous methods would be impractical or ineffective. We demonstrate that our methods are effective against an ImageNet classifier under our proposed threat models. We also demonstrate a targeted black-box attack against a commercial classifier, overcoming the challenges of limited query access, partial information, and other practical issues to break the Google Cloud Vision API.
Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model
Authors: Imamura, Hideaki and Sato, Issei and Sugiyama, Masashi
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: While crowdsourcing has become an important means to label data, there is great interest in estimating the ground truth from unreliable labels produced by crowdworkers. The Dawid and Skene (DS) model is one of the most well-known models in the study of crowdsourcing. Despite its practical popularity, theoretical error analysis for the DS model has been conducted only under restrictive assumptions on class priors, confusion matrices, or the number of labels each worker provides. In this paper, we derive a minimax error rate under more practical setting for a broader class of crowdsourcing models including the DS model as a special case. We further propose the worker clustering model, which is more practical than the DS model under real crowdsourcing settings. The wide applicability of our theoretical analysis allows us to immediately investigate the behavior of this proposed model, which can not be analyzed by existing studies. Experimental results showed that there is a strong similarity between the lower bound of the minimax error rate derived by our theoretical analysis and the empirical error of the estimated value.
Improving Regression Performance with Distributional Losses
Authors: Imani, Ehsan and White, Martha
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: There is growing evidence that converting targets to soft targets in supervised learning can provide considerable gains in performance. Much of this work has considered classification, converting hard zero-one values to soft labels—such as by adding label noise, incorporating label ambiguity or using distillation. In parallel, there is some evidence from a regression setting in reinforcement learning that learning distributions can improve performance. In this work, we investigate the reasons for this improvement, in a regression setting. We introduce a novel distributional regression loss, and similarly find it significantly improves prediction accuracy. We investigate several common hypotheses, around reducing overfitting and improved representations. We instead find evidence for an alternative hypothesis: this loss is easier to optimize, with better behaved gradients, resulting in improved generalization. We provide theoretical support for this alternative hypothesis, by characterizing the norm of the gradients of this loss.
Deep Density Destructors
Authors: Inouye, David and Ravikumar, Pradeep
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We propose a unified framework for deep density models by formally defining density destructors. A density destructor is an invertible function that transforms a given density to the uniform density—essentially destroying any structure in the original density. This destructive transformation generalizes Gaussianization via ICA and more recent autoregressive models such as MAF and Real NVP. Informally, this transformation can be seen as a generalized whitening procedure or a multivariate generalization of the univariate CDF function. Unlike Gaussianization, our destructive transformation has the elegant property that the density function is equal to the absolute value of the Jacobian determinant. Thus, each layer of a deep density can be seen as a shallow density—uncovering a fundamental connection between shallow and deep densities. In addition, our framework provides a common interface for all previous methods enabling them to be systematically combined, evaluated and improved. Leveraging the connection to shallow densities, we also propose a novel tree destructor based on tree densities and an image-specific destructor based on pixel locality. We illustrate our framework on a 2D dataset, MNIST, and CIFAR-10. Code is available on first author’s website.
Unbiased Objective Estimation in Predictive Optimization
Authors: Ito, Shinji and Yabe, Akihiro and Fujimaki, Ryohei
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: For data-driven decision-making, one promising approach, called predictive optimization, is to solve maximization problems i n which the objective function to be maximized is estimated from data. Predictive optimization, however, suffers from the problem of a calculated optimal solution’s being evaluated too optimistically, i.e., the value of the objective function is overestimated. This paper investigates such optimistic bias and presents two methods for correcting it. The first, which is analogous to cross-validation, successfully corrects the optimistic bias but results in underestimation of the true value. Our second method employs resampling techniques to avoid both overestimation and underestimation. We show that the second method, referred to as the parameter perturbation method, achieves asymptotically unbiased estimation. Empirical results for both artificial and real-world datasets demonstrate that our proposed approach successfully corrects the optimistic bias.
Anonymous Walk Embeddings
Authors: Ivanov, Sergey and Burnaev, Evgeny
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: The task of representing entire graphs has seen a surge of prominent results, mainly due to learning convolutional neural networks (CNNs) on graph-structured data. While CNNs demonstrate state-of-the-art performance in graph classification task, such methods are supervised and therefore steer away from the original problem of network representation in task-agnostic manner. Here, we coherently propose an approach for embedding entire graphs and show that our feature representations with SVM classifier increase classification accuracy of CNN algorithms and traditional graph kernels. For this we describe a recently discovered graph object, <em>anonymous walk</em>, on which we design task-independent algorithms for learning graph representations in explicit and distributed way. Overall, our work represents a new scalable unsupervised learning of state-of-the-art representations of entire graphs.
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Authors: Jaffe, Ariel and Weiss, Roi and Nadler, Boaz and Carmi, Shai and Kluger, Yuval
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Latent variable models with hidden binary units appear in various applications. Learning such models, in particular in the presence of noise, is a challenging computational problem. In this paper we propose a novel spectral approach to this problem, based on the eigenvectors of both the second order moment matrix and third order moment tensor of the observed data. We prove that under mild non-degeneracy conditions, our method consistently estimates the model parameters at the optimal parametric rate. Our tensor-based method generalizes previous orthogonal tensor decomposition approaches, where the hidden units were assumed to be either statistically independent or mutually exclusive. We illustrate the consistency of our method on simulated data and demonstrate its usefulness in learning a common model for population mixtures in genetics.
Firing Bandits: Optimizing Crowdfunding
Authors: Jain, Lalit and Jamieson, Kevin
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: In this paper, we model the problem of optimizing crowdfunding platforms, such as the non-profit Kiva or for-profit KickStarter, as a variant of the multi-armed bandit problem. In our setting, Bernoulli arms emit no rewards until their cumulative number of successes over any number of trials exceeds a fixed threshold and then provides no additional reward for any additional trials - a process reminiscent to that of a neuron firing once it reaches the action potential and then saturates. In the spirit of an infinite armed bandit problem, the player can add new arms whose expected probability of success is drawn iid from an unknown distribution – this endless supply of projects models the harsh reality that the number of projects seeking funding greatly exceeds the total capital available by lenders. Crowdfunding platforms naturally fall under this setting where the arms are potential projects, and their probability of success is the probability that a potential funder decides to fund it after reviewing it. The goal is to play arms (prioritize the display of projects on a webpage) to maximize the number of arms that reach the firing threshold (meet their goal amount) using as few total trials (number of impressions) as possible over all the played arms. We provide an algorithm for this setting and prove sublinear regret bounds.
Differentially Private Matrix Completion Revisited
Authors: Jain, Prateek and Thakkar, Om Dipakbhai and Thakurta, Abhradeep
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We provide the first provably joint differentially private algorithm with formal utility guarantees for the problem of user-level privacy-preserving collaborative filtering. Our algorithm is based on the Frank-Wolfe method, and it consistently estimates the underlying preference matrix as long as the number of users $m$ is $ømega(n^5/4)$, where $n$ is the number of items, and each user provides her preference for at least $\sqrtn$ randomly selected items. Along the way, we provide an optimal differentially private algorithm for singular vector computation, based on the celebrated Oja’s method, that provides significant savings in terms of space and time while operating on sparse matrices. We also empirically evaluate our algorithm on a suite of datasets, and show that it consistently outperforms the state-of-the-art private algorithms.
Video Prediction with Appearance and Motion Conditions
Authors: Jang, Yunseok and Kim, Gunhee and Song, Yale
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Video prediction aims to generate realistic future frames by learning dynamic visual patterns. One fundamental challenge is to deal with future uncertainty: How should a model behave when there are multiple correct, equally probable future? We propose an Appearance-Motion Conditional GAN to address this challenge. We provide appearance and motion information as conditions that specify how the future may look like, reducing the level of uncertainty. Our model consists of a generator, two discriminators taking charge of appearance and motion pathways, and a perceptual ranking module that encourages videos of similar conditions to look similar. To train our model, we develop a novel conditioning scheme that consists of different combinations of appearance and motion conditions. We evaluate our model using facial expression and human action datasets and report favorable results compared to existing methods.
Pathwise Derivatives Beyond the Reparameterization Trick
Authors: Jankowiak, Martin and Obermeyer, Fritz
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We observe that gradients computed via the reparameterization trick are in direct correspondence with solutions of the transport equation in the formalism of optimal transport. We use this perspective to compute (approximate) pathwise gradients for probability distributions not directly amenable to the reparameterization trick: Gamma, Beta, and Dirichlet. We further observe that when the reparameterization trick is applied to the Cholesky-factorized multivariate Normal distribution, the resulting gradients are suboptimal in the sense of optimal transport. We derive the optimal gradients and show that they have reduced variance in a Gaussian Process regression task. We demonstrate with a variety of synthetic experiments and stochastic variational inference tasks that our pathwise gradients are competitive with other methods.
Detecting non-causal artifacts in multivariate linear regression models
Authors: Janzing, Dominik and Schölkopf, Bernhard
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We consider linear models where d potential causes X_1,...,X_d are correlated with one target quantity Y and propose a method to infer whether the association is causal or whether it is an artifact caused by overfitting or hidden common causes. We employ the idea that in the former case the vector of regression coefficients has ‘generic’ orientation relative to the covariance matrix Sigma_XX of X. Using an ICA based model for confounding, we show that both confounding and overfitting yield regression vectors that concentrate mainly in the space of low eigenvalues of Sigma_XX.
A Unified Framework for Structured Low-rank Matrix Learning
Authors: Jawanpuria, Pratik and Mishra, Bamdev
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We consider the problem of learning a low-rank matrix, constrained to lie in a linear subspace, and introduce a novel factorization for modeling such matrices. A salient feature of the proposed factorization scheme is it decouples the low-rank and the structural constraints onto separate factors. We formulate the optimization problem on the Riemannian spectrahedron manifold, where the Riemannian framework allows to develop computationally efficient conjugate gradient and trust-region algorithms. Experiments on problems such as standard/robust/non-negative matrix completion, Hankel matrix learning and multi-task learning demonstrate the efficacy of our approach.
Efficient end-to-end learning for quantizable representations
Authors: Jeong, Yeonwoo and Song, Hyun Oh
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Embedding representation learning via neural networks is at the core foundation of modern similarity based search. While much effort has been put in developing algorithms for learning binary hamming code representations for search efficiency, this still requires a linear scan of the entire dataset per each query and trades off the search accuracy through binarization. To this end, we consider the problem of directly learning a quantizable embedding representation and the sparse binary hash code end-to-end which can be used to construct an efficient hash table not only providing significant search reduction in the number of data but also achieving the state of the art search accuracy outperforming previous state of the art deep metric learning methods. We also show that finding the optimal sparse binary hash code in a mini-batch can be computed exactly in polynomial time by solving a minimum cost flow problem. Our results on Cifar-100 and on ImageNet datasets show the state of the art search accuracy in precision@k and NMI metrics while providing up to 98X and 478X search speedup respectively over exhaustive linear search. The source code is available at https://github.com/maestrojeong/Deep-Hash-Table-ICML18.
Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks
Authors: Jia, Zhihao and Lin, Sina and Qi, Charles R. and Aiken, Alex
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: The past few years have witnessed growth in the computational requirements for training deep convolutional neural networks. Current approaches parallelize training onto multiple devices by applying a single parallelization strategy (e.g., data or model parallelism) to all layers in a network. Although easy to reason about, these approaches result in suboptimal runtime performance in large-scale distributed training, since different layers in a network may prefer different parallelization strategies. In this paper, we propose layer-wise parallelism that allows each layer in a network to use an individual parallelization strategy. We jointly optimize how each layer is parallelized by solving a graph search problem. Our evaluation shows that layer-wise parallelism outperforms state-of-the-art approaches by increasing training throughput, reducing communication costs, achieving better scalability to multiple GPUs, while maintaining original network accuracy.
Feedback-Based Tree Search for Reinforcement Learning
Authors: Jiang, Daniel and Ekwedike, Emmanuel and Liu, Han
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Inspired by recent successes of Monte-Carlo tree search (MCTS) in a number of artificial intelligence (AI) application domains, we propose a reinforcement learning (RL) technique that iteratively applies MCTS on batches of small, finite-horizon versions of the original infinite-horizon Markov decision process. The terminal condition of the finite-horizon problems, or the leaf-node evaluator of the decision tree generated by MCTS, is specified using a combination of an estimated value function and an estimated policy function. The recommendations generated by the MCTS procedure are then provided as feedback in order to refine, through classification and regression, the leaf-node evaluator for the next iteration. We provide the first sample complexity bounds for a tree search-based RL algorithm. In addition, we show that a deep neural network implementation of the technique can create a competitive AI agent for the popular multi-player online battle arena (MOBA) game King of Glory.
Quickshift++: Provably Good Initializations for Sample-Based Mean Shift
Authors: Jiang, Heinrich and Jang, Jennifer and Kpotufe, Samory
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We provide initial seedings to the Quick Shift clustering algorithm, which approximate the locally high-density regions of the data. Such seedings act as more stable and expressive cluster-cores than the singleton modes found by Quick Shift. We establish statistical consistency guarantees for this modification. We then show strong clustering performance on real datasets as well as promising applications to image segmentation.
MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Authors: Jiang, Lu and Zhou, Zhengyuan and Leung, Thomas and Li, Li-Jia and Fei-Fei, Li
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: Recent deep networks are capable of memorizing the entire data even when the labels are completely random. To overcome the overfitting on corrupted labels, we propose a novel technique of learning another neural network, called MentorNet, to supervise the training of the base deep networks, namely, StudentNet. During training, MentorNet provides a curriculum (sample weighting scheme) for StudentNet to focus on the sample the label of which is probably correct. Unlike the existing curriculum that is usually predefined by human experts, MentorNet learns a data-driven curriculum dynamically with StudentNet. Experimental results demonstrate that our approach can significantly improve the generalization performance of deep networks trained on corrupted training data. Notably, to the best of our knowledge, we achieve the best-published result on WebVision, a large benchmark containing 2.2 million images of real-world noisy labels.
The Weighted Kendall and High-order Kernels for Permutations
Authors: Jiao, Yunlong and Vert, Jean-Philippe
Published in: Proceedings of the 35th International Conference on Machine Learning (Main Conference Track), 2018

Abstract: We propose new positive definite kernels for permutations. First we introduce a weighted version of the Kendall kernel, which allows to weight unequally the contributions of different item pairs in the permutations depending on their ranks. Like the Kendall kernel, we show that the weighted version is invariant to relabeling of items and can be computed efficiently in O(n ln(n)) operations, where n is the number of items in the permutation. Second, we propose a supervised approach to learn the weights by jointly optimizing them with the function estimated by a kernel machine. Third, while the Kendall kernel considers pairwise comparison between items, we extend it by considering higher-order comparisons among tuples of items and show that the supervised approach of learning the weights can be systematically generalized to higher-order permutation kernels.