pwc-archive/papers-with-abstracts huggingface dataset:
Complex systems: features, similarity and connectivity
Authors: Cesar H. Comin, Thomas K. DM. Peron, Filipi N. Silva, Diego R. Amancio, Francisco A. Rodrigues, Luciano da F. Costa
Date: 2016-06-17 00:00:00
Tasks: Sociology, Time Series Analysis

The increasing interest in complex networks research has been a consequence
of several intrinsic features of this area, such as the generality of the
approach to represent and model virtually any discrete system, and the
incorporation of concepts and methods deriving from many areas, from
statistical physics to sociology, which are often used in an independent way.
Yet, for this same reason, it would be desirable to integrate these various
aspects into a more coherent and organic framework, which would imply in
several benefits normally allowed by the systematization in science, including
the identification of new types of problems and the cross-fertilization between
fields. More specifically, the identification of the main areas to which the
concepts frequently used in complex networks can be applied paves the way to
adopting and applying a larger set of concepts and methods deriving from those
respective areas. Among the several areas that have been used in complex
networks research, pattern recognition, optimization, linear algebra, and time
series analysis seem to play a more basic and recurrent role. In the present
manuscript, we propose a systematic way to integrate the concepts from these
diverse areas regarding complex networks research. In order to do so, we start
by grouping the multidisciplinary concepts into three main groups, namely
features, similarity, and network connectivity. Then we show that several of
the analysis and modeling approaches to complex networks can be thought as a
composition of maps between these three groups, with emphasis on nine main
types of mappings, which are presented and illustrated. Such a systematization
of principles and approaches also provides an opportunity to review some of the
most closely related works in the literature, which is also developed in this
article.
Making Tree Ensembles Interpretable
Authors: Satoshi Hara, Kohei Hayashi
Date: 2016-06-17 00:00:00

Tree ensembles, such as random forest and boosted trees, are renowned for
their high prediction performance, whereas their interpretability is critically
limited. In this paper, we propose a post processing method that improves the
model interpretability of tree ensembles. After learning a complex tree
ensembles in a standard way, we approximate it by a simpler model that is
interpretable for human. To obtain the simpler model, we derive the EM
algorithm minimizing the KL divergence from the complex ensemble. A synthetic
experiment showed that a complicated tree ensemble was approximated reasonably
as interpretable.
Model-Agnostic Interpretability of Machine Learning
Authors: Marco Tulio Ribeiro, Sameer Singh, Carlos Guestrin
Date: 2016-06-16 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, model, Model Selection

Understanding why machine learning models behave the way they do empowers
both system designers and end-users in many ways: in model selection, feature
engineering, in order to trust and act upon the predictions, and in more
intuitive user interfaces. Thus, interpretability has become a vital concern in
machine learning, and work in the area of interpretable models has found
renewed interest. In some applications, such models are as accurate as
non-interpretable ones, and thus are preferred for their transparency. Even
when they are not accurate, they may still be preferred when interpretability
is of paramount importance. However, restricting machine learning to
interpretable models is often a severe limitation. In this paper we argue for
explaining machine learning predictions using model-agnostic approaches. By
treating the machine learning models as black-box functions, these approaches
provide crucial flexibility in the choice of models, explanations, and
representations, improving debugging, comparison, and interfaces for a variety
of users and models. We also outline the main challenges for such methods, and
review a recently-introduced model-agnostic explanation approach (LIME) that
addresses these challenges.
Simpler Context-Dependent Logical Forms via Model Projections
Authors: Reginald Long, Panupong Pasupat, Percy Liang
Date: 2016-06-16 00:00:00
Proceeding: ACL 2016 8
Tasks: model, Semantic Parsing

We consider the task of learning a context-dependent mapping from utterances
to denotations. With only denotations at training time, we must search over a
combinatorially large space of logical forms, which is even larger with
context-dependent utterances. To cope with this challenge, we perform
successive projections of the full model onto simpler models that operate over
equivalence classes of logical forms. Though less expressive, we find that
these simpler models are much faster and can be surprisingly effective.
Moreover, they can be used to bootstrap the full model. Finally, we collected
three new context-dependent semantic parsing datasets, and develop a new
left-to-right parser.
Avoiding Imposters and Delinquents: Adversarial Crowdsourcing and Peer Prediction
Authors: Jacob Steinhardt, Gregory Valiant, Moses Charikar
Date: 2016-06-16 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Prediction

We consider a crowdsourcing model in which $n$ workers are asked to rate the
quality of $n$ items previously generated by other workers. An unknown set of
$\alpha n$ workers generate reliable ratings, while the remaining workers may
behave arbitrarily and possibly adversarially. The manager of the experiment
can also manually evaluate the quality of a small number of items, and wishes
to curate together almost all of the high-quality items with at most an
$\epsilon$ fraction of low-quality items. Perhaps surprisingly, we show that
this is possible with an amount of work required of the manager, and each
worker, that does not scale with $n$: the dataset can be curated with
$\tilde{O}\Big(\frac{1}{\beta\alpha^3\epsilon^4}\Big)$ ratings per worker, and
$\tilde{O}\Big(\frac{1}{\beta\epsilon^2}\Big)$ ratings by the manager, where
$\beta$ is the fraction of high-quality items. Our results extend to the more
general setting of peer prediction, including peer grading in online
classrooms.
Evolution of active categorical image classification via saccadic eye movement
Authors: Randal S. Olson, Jason H. Moore, Christoph Adami
Date: 2016-03-27 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification, Video Classification

Pattern recognition and classification is a central concern for modern
information processing systems. In particular, one key challenge to image and
video classification has been that the computational cost of image processing
scales linearly with the number of pixels in the image or video. Here we
present an intelligent machine (the "active categorical classifier," or ACC)
that is inspired by the saccadic movements of the eye, and is capable of
classifying images by selectively scanning only a portion of the image. We
harness evolutionary computation to optimize the ACC on the MNIST hand-written
digit classification task, and provide a proof-of-concept that the ACC works on
noisy multi-class data. We further analyze the ACC and demonstrate its ability
to classify images after viewing only a fraction of the pixels, and provide
insight on future research paths to further improve upon the ACC presented
here.
Ensuring Rapid Mixing and Low Bias for Asynchronous Gibbs Sampling
Authors: Christopher De Sa, Kunle Olukotun, Christopher Ré
Date: 2016-02-24 00:00:00

Gibbs sampling is a Markov chain Monte Carlo technique commonly used for
estimating marginal distributions. To speed up Gibbs sampling, there has
recently been interest in parallelizing it by executing asynchronously. While
empirical results suggest that many models can be efficiently sampled
asynchronously, traditional Markov chain analysis does not apply to the
asynchronous case, and thus asynchronous Gibbs sampling is poorly understood.
In this paper, we derive a better understanding of the two main challenges of
asynchronous Gibbs: bias and mixing time. We show experimentally that our
theoretical results match practical outcomes.
Exact Bayesian inference for off-line change-point detection in tree-structured graphical models
Authors: Loïc Schwaller, Stéphane Robin
Date: 2016-03-25 00:00:00
Tasks: Bayesian Inference, Change Point Detection, Time Series, Time Series Analysis

We consider the problem of change-point detection in multivariate
time-series. The multivariate distribution of the observations is supposed to
follow a graphical model, whose graph and parameters are affected by abrupt
changes throughout time. We demonstrate that it is possible to perform exact
Bayesian inference whenever one considers a simple class of undirected graphs
called spanning trees as possible structures. We are then able to integrate on
the graph and segmentation spaces at the same time by combining classical
dynamic programming with algebraic results pertaining to spanning trees. In
particular, we show that quantities such as posterior distributions for
change-points or posterior edge probabilities over time can efficiently be
obtained. We illustrate our results on both synthetic and experimental data
arising from biology and neuroscience.
Predicting Ambulance Demand: Challenges and Methods
Authors: Zhengyi Zhou
Date: 2016-06-16 00:00:00
Tasks: Density Estimation, Management

Predicting ambulance demand accurately at a fine resolution in time and space
(e.g., every hour and 1 km$^2$) is critical for staff / fleet management and
dynamic deployment. There are several challenges: though the dataset is
typically large-scale, demand per time period and locality is almost always
zero. The demand arises from complex urban geography and exhibits complex
spatio-temporal patterns, both of which need to captured and exploited. To
address these challenges, we propose three methods based on Gaussian mixture
models, kernel density estimation, and kernel warping. These methods provide
spatio-temporal predictions for Toronto and Melbourne that are significantly
more accurate than the current industry practice.
Covariance of Motion and Appearance Featuresfor Spatio Temporal Recognition Tasks
Authors: Subhabrata Bhattacharya, Nasim Souly, Mubarak Shah
Date: 2016-06-16 00:00:00
Tasks: Gesture Recognition, One-Shot Learning, Optical Flow Estimation

In this paper, we introduce an end-to-end framework for video analysis
focused towards practical scenarios built on theoretical foundations from
sparse representation, including a novel descriptor for general purpose video
analysis. In our approach, we compute kinematic features from optical flow and
first and second-order derivatives of intensities to represent motion and
appearance respectively. These features are then used to construct covariance
matrices which capture joint statistics of both low-level motion and appearance
features extracted from a video. Using an over-complete dictionary of the
covariance based descriptors built from labeled training samples, we formulate
low-level event recognition as a sparse linear approximation problem. Within
this, we pose the sparse decomposition of a covariance matrix, which also
conforms to the space of semi-positive definite matrices, as a determinant
maximization problem. Also since covariance matrices lie on non-linear
Riemannian manifolds, we compare our former approach with a sparse linear
approximation alternative that is suitable for equivalent vector spaces of
covariance matrices. This is done by searching for the best projection of the
query data on a dictionary using an Orthogonal Matching pursuit algorithm. We
show the applicability of our video descriptor in two different application
domains - namely low-level event recognition in unconstrained scenarios and
gesture recognition using one shot learning. Our experiments provide promising
insights in large scale video analysis.
ACDC: $α$-Carving Decision Chain for Risk Stratification
Authors: Yubin Park, Joyce Ho, Joydeep Ghosh
Date: 2016-06-16 00:00:00

In many healthcare settings, intuitive decision rules for risk stratification
can help effective hospital resource allocation. This paper introduces a novel
variant of decision tree algorithms that produces a chain of decisions, not a
general tree. Our algorithm, $\alpha$-Carving Decision Chain (ACDC),
sequentially carves out "pure" subsets of the majority class examples. The
resulting chain of decision rules yields a pure subset of the minority class
examples. Our approach is particularly effective in exploring large and
class-imbalanced health datasets. Moreover, ACDC provides an interactive
interpretation in conjunction with visual performance metrics such as Receiver
Operating Characteristics curve and Lift chart.
Unsupervised Risk Estimation Using Only Conditional Independence Structure
Authors: Jacob Steinhardt, Percy Liang
Date: 2016-06-16 00:00:00
Proceeding: NeurIPS 2016 12

We show how to estimate a model's test error from unlabeled data, on
distributions very different from the training distribution, while assuming
only that certain conditional independencies are preserved between train and
test. We do not need to assume that the optimal predictor is the same between
train and test, or that the true distribution lies in any parametric family. We
can also efficiently differentiate the error estimate to perform unsupervised
discriminative learning. Our technical tool is the method of moments, which
allows us to exploit conditional independencies in the absence of a
fully-specified model. Our framework encompasses a large family of losses
including the log and exponential loss, and extends to structured output
settings such as hidden Markov models.
Holistic Features For Real-Time Crowd Behaviour Anomaly Detection
Authors: M. Marsden, K. McGuinness, S. Little, N. E. O'Connor
Date: 2016-06-16 00:00:00
Tasks: Anomaly Detection, Binary Classification, General Classification, Outlier Detection

This paper presents a new approach to crowd behaviour anomaly detection that
uses a set of efficiently computed, easily interpretable, scene-level holistic
features. This low-dimensional descriptor combines two features from the
literature: crowd collectiveness [1] and crowd conflict [2], with two newly
developed crowd features: mean motion speed and a new formulation of crowd
density. Two different anomaly detection approaches are investigated using
these features. When only normal training data is available we use a Gaussian
Mixture Model (GMM) for outlier detection. When both normal and abnormal
training data is available we use a Support Vector Machine (SVM) for binary
classification. We evaluate on two crowd behaviour anomaly detection datasets,
achieving both state-of-the-art classification performance on the violent-flows
dataset [3] as well as better than real-time processing performance (40 frames
per second).
Generalized Direct Change Estimation in Ising Model Structure
Authors: Farideh Fazayeli, Arindam Banerjee
Date: 2016-06-16 00:00:00
Tasks: model

We consider the problem of estimating change in the dependency structure
between two $p$-dimensional Ising models, based on respectively $n_1$ and $n_2$
samples drawn from the models. The change is assumed to be structured, e.g.,
sparse, block sparse, node-perturbed sparse, etc., such that it can be
characterized by a suitable (atomic) norm. We present and analyze a
norm-regularized estimator for directly estimating the change in structure,
without having to estimate the structures of the individual Ising models. The
estimator can work with any norm, and can be generalized to other graphical
models under mild assumptions. We show that only one set of samples, say $n_2$,
needs to satisfy the sample complexity requirement for the estimator to work,
and the estimation error decreases as $\frac{c}{\sqrt{\min(n_1,n_2)}}$, where
$c$ depends on the Gaussian width of the unit norm ball. For example, for
$\ell_1$ norm applied to $s$-sparse change, the change can be accurately
estimated with $\min(n_1,n_2)=O(s \log p)$ which is sharper than an existing
result $n_1= O(s^2 \log p)$ and $n_2 = O(n_1^2)$. Experimental results
illustrating the effectiveness of the proposed estimator are presented.
Stacking for machine learning redshifts applied to SDSS galaxies
Authors: Roman Zitlau, Ben Hoyle, Kerstin Paech, Jochen Weller, Markus Michael Rau, Stella Seitz
Date: 2016-02-19 00:00:00
Tasks: BIG-bench Machine Learning

We present an analysis of a general machine learning technique called
'stacking' for the estimation of photometric redshifts. Stacking techniques can
feed the photometric redshift estimate, as output by a base algorithm, back
into the same algorithm as an additional input feature in a subsequent learning
round. We shown how all tested base algorithms benefit from at least one
additional stacking round (or layer). To demonstrate the benefit of stacking,
we apply the method to both unsupervised machine learning techniques based on
self-organising maps (SOMs), and supervised machine learning methods based on
decision trees. We explore a range of stacking architectures, such as the
number of layers and the number of base learners per layer. Finally we explore
the effectiveness of stacking even when using a successful algorithm such as
AdaBoost. We observe a significant improvement of between 1.9% and 21% on all
computed metrics when stacking is applied to weak learners (such as SOMs and
decision trees). When applied to strong learning algorithms (such as AdaBoost)
the ratio of improvement shrinks, but still remains positive and is between
0.4% and 2.5% for the explored metrics and comes at almost no additional
computational cost.
Spectral decomposition method of dialog state tracking via collective matrix factorization
Authors: Julien Perez
Date: 2016-06-16 00:00:00
Tasks: dialog state tracking, Management, Natural Language Understanding, speech-recognition, Speech Recognition

The task of dialog management is commonly decomposed into two sequential
subtasks: dialog state tracking and dialog policy learning. In an end-to-end
dialog system, the aim of dialog state tracking is to accurately estimate the
true dialog state from noisy observations produced by the speech recognition
and the natural language understanding modules. The state tracking task is
primarily meant to support a dialog policy. From a probabilistic perspective,
this is achieved by maintaining a posterior distribution over hidden dialog
states composed of a set of context dependent variables. Once a dialog policy
is learned, it strives to select an optimal dialog act given the estimated
dialog state and a defined reward function. This paper introduces a novel
method of dialog state tracking based on a bilinear algebric decomposition
model that provides an efficient inference schema through collective matrix
factorization. We evaluate the proposed approach on the second Dialog State
Tracking Challenge (DSTC-2) dataset and we show that the proposed tracker gives
encouraging results compared to the state-of-the-art trackers that participated
in this standard benchmark. Finally, we show that the prediction schema is
computationally efficient in comparison to the previous approaches.
Designing Intelligent Automation based Solutions for Complex Social Problems
Authors: Sanjay Podder, Janardan Misra, Senthil Kumaresan, Neville Dubash, Indrani Bhattacharya
Date: 2016-06-16 00:00:00
Tasks: BIG-bench Machine Learning

Deciding effective and timely preventive measures against complex social
problems affecting relatively low income geographies is a difficult challenge.
There is a strong need to adopt intelligent automation based solutions with low
cost imprints to tackle these problems at larger scales. Starting with the
hypothesis that analytical modelling and analysis of social phenomena with high
accuracy is in general inherently hard, in this paper we propose design
framework to enable data-driven machine learning based adaptive solution
approach towards enabling more effective preventive measures. We use survey
data collected from a socio-economically backward region of India about
adolescent girls to illustrate the design approach.
The Effect of Heteroscedasticity on Regression Trees
Authors: Will Ruth, Thomas Loughin
Date: 2016-06-16 00:00:00
Tasks: regression

Regression trees are becoming increasingly popular as omnibus predicting
tools and as the basis of numerous modern statistical learning ensembles. Part
of their popularity is their ability to create a regression prediction without
ever specifying a structure for the mean model. However, the method implicitly
assumes homogeneous variance across the entire explanatory-variable space. It
is unknown how the algorithm behaves when faced with heteroscedastic data. In
this study, we assess the performance of the most popular regression-tree
algorithm in a single-variable setting under a very simple step-function model
for heteroscedasticity. We use simulation to show that the locations of splits,
and hence the ability to accurately predict means, are both adversely
influenced by the change in variance. We identify the pruning algorithm as the
main concern, although the effects on the splitting algorithm may be meaningful
in some applications.
Collaborative Multi-sensor Classification via Sparsity-based Representation
Authors: Minh Dao, Nam H. Nguyen, Nasser M. Nasrabadi, Trac. D. Tran
Date: 2014-10-29 00:00:00
Tasks: Classification, General Classification

In this paper, we propose a general collaborative sparse representation
framework for multi-sensor classification, which takes into account the
correlations as well as complementary information between heterogeneous sensors
simultaneously while considering joint sparsity within each sensor's
observations. We also robustify our models to deal with the presence of sparse
noise and low-rank interference signals. Specifically, we demonstrate that
incorporating the noise or interference signal as a low-rank component in our
models is essential in a multi-sensor classification problem when multiple
co-located sources/sensors simultaneously record the same physical event. We
further extend our frameworks to kernelized models which rely on sparsely
representing a test sample in terms of all the training samples in a feature
space induced by a kernel function. A fast and efficient algorithm based on
alternative direction method is proposed where its convergence to an optimal
solution is guaranteed. Extensive experiments are conducted on several real
multi-sensor data sets and results are compared with the conventional
classifiers to verify the effectiveness of the proposed methods.
3D zigzag for multislicing, multiband and video processing
Authors: Mario Mastriani
Date: 2016-06-16 00:00:00

We present a 3D zigzag rafter (first in literature) which allows us to obtain
the exact sequence of spectral components after application of Discrete Cosine
Transform 3D (DCT-2D) over a cube. Such cube represents part of a video or
eventually a group of images such as multislicing (e.g., Magnetic Resonance or
Computed Tomography imaging) and multi or hyperspectral imagery (optical
satellites). Besides, we present a new version of the traditional 2D zigzag,
including the case of rectangular blocks. Finally, all the attached code is
done in MATLAB, and that code serves both blocks of pixels or blocks of blocks.