pwc-archive/papers-with-abstracts huggingface dataset:
Determination of hysteresis in finite-state random walks using Bayesian cross validation
Authors: Joshua C. Chang
Date: 2017-02-21 00:00:00

Consider the problem of modeling hysteresis for finite-state random walks
using higher-order Markov chains. This Letter introduces a Bayesian framework
to determine, from data, the number of prior states of recent history upon
which a trajectory is statistically dependent. The general recommendation is to
use leave-one-out cross validation, using an easily-computable formula that is
provided in closed form. Importantly, Bayes factors using flat model priors are
biased in favor of too-complex a model (more hysteresis) when a large amount of
data is present and the Akaike information criterion (AIC) is biased in favor
of too-sparse a model (less hysteresis) when few data are present.
An Online Optimization Approach for Multi-Agent Tracking of Dynamic Parameters in the Presence of Adversarial Noise
Authors: Shahin Shahrampour, Ali Jadbabaie
Date: 2017-02-21 00:00:00
Tasks: Distributed Optimization

This paper addresses tracking of a moving target in a multi-agent network.
The target follows a linear dynamics corrupted by an adversarial noise, i.e.,
the noise is not generated from a statistical distribution. The location of the
target at each time induces a global time-varying loss function, and the global
loss is a sum of local losses, each of which is associated to one agent. Agents
noisy observations could be nonlinear. We formulate this problem as a
distributed online optimization where agents communicate with each other to
track the minimizer of the global loss. We then propose a decentralized version
of the Mirror Descent algorithm and provide the non-asymptotic analysis of the
problem. Using the notion of dynamic regret, we measure the performance of our
algorithm versus its offline counterpart in the centralized setting. We prove
that the bound on dynamic regret scales inversely in the network spectral gap,
and it represents the adversarial noise causing deviation with respect to the
linear dynamics. Our result subsumes a number of results in the distributed
optimization literature. Finally, in a numerical experiment, we verify that our
algorithm can be simply implemented for multi-agent tracking with nonlinear
observations.
RenderMap: Exploiting the Link Between Perception and Rendering for Dense Mapping
Authors: Julian Ryde, Xuchu, Ding
Date: 2017-02-21 00:00:00
Tasks: GPU

We introduce an approach for the real-time (2Hz) creation of a dense map and
alignment of a moving robotic agent within that map by rendering using a
Graphics Processing Unit (GPU). This is done by recasting the scan alignment
part of the dense mapping process as a rendering task. Alignment errors are
computed from rendering the scene, comparing with range data from the sensors,
and minimized by an optimizer. The proposed approach takes advantage of the
advances in rendering techniques for computer graphics and GPU hardware to
accelerate the algorithm. Moreover, it allows one to exploit information not
used in classic dense mapping algorithms such as Iterative Closest Point (ICP)
by rendering interfaces between the free space, occupied space and the unknown.
The proposed approach leverages directly the rendering capabilities of the GPU,
in contrast to other GPU-based approaches that deploy the GPU as a general
purpose parallel computation platform.
  We argue that the proposed concept is a general consequence of treating
perception problems as inverse problems of rendering. Many perception problems
can be recast into a form where much of the computation is replaced by render
operations. This is not only efficient since rendering is fast, but also
simpler to implement and will naturally benefit from future advancements in GPU
speed and rendering techniques. Furthermore, this general concept can go beyond
addressing perception problems and can be used for other problem domains such
as path planning.
Efficient Dense Labeling of Human Activity Sequences from Wearables using Fully Convolutional Networks
Authors: Rui Yao, Guosheng Lin, Qinfeng Shi, Damith Ranasinghe
Date: 2017-02-20 00:00:00

Recognizing human activities in a sequence is a challenging area of research
in ubiquitous computing. Most approaches use a fixed size sliding window over
consecutive samples to extract features---either handcrafted or learned
features---and predict a single label for all samples in the window. Two key
problems emanate from this approach: i) the samples in one window may not
always share the same label. Consequently, using one label for all samples
within a window inevitably lead to loss of information; ii) the testing phase
is constrained by the window size selected during training while the best
window size is difficult to tune in practice. We propose an efficient algorithm
that can predict the label of each sample, which we call dense labeling, in a
sequence of human activities of arbitrary length using a fully convolutional
network. In particular, our approach overcomes the problems posed by the
sliding window step. Additionally, our algorithm learns both the features and
classifier automatically. We release a new daily activity dataset based on a
wearable sensor with hospitalized patients. We conduct extensive experiments
and demonstrate that our proposed approach is able to outperform the
state-of-the-arts in terms of classification and label misalignment measures on
three challenging datasets: Opportunity, Hand Gesture, and our new dataset.
Uniform Inference for High-dimensional Quantile Regression: Linear Functionals and Regression Rank Scores
Authors: Jelena Bradic, Mladen Kolar
Date: 2017-02-20 00:00:00
Tasks: quantile regression, regression, valid

Hypothesis tests in models whose dimension far exceeds the sample size can be
formulated much like the classical studentized tests only after the initial
bias of estimation is removed successfully. The theory of debiased estimators
can be developed in the context of quantile regression models for a fixed
quantile value. However, it is frequently desirable to formulate tests based on
the quantile regression process, as this leads to more robust tests and more
stable confidence sets. Additionally, inference in quantile regression requires
estimation of the so called sparsity function, which depends on the unknown
density of the error. In this paper we consider a debiasing approach for the
uniform testing problem. We develop high-dimensional regression rank scores and
show how to use them to estimate the sparsity function, as well as how to adapt
them for inference involving the quantile regression process. Furthermore, we
develop a Kolmogorov-Smirnov test in a location-shift high-dimensional models
and confidence sets that are uniformly valid for many quantile values. The main
technical result are the development of a Bahadur representation of the
debiasing estimator that is uniform over a range of quantiles and uniform
convergence of the quantile process to the Brownian bridge process, which are
of independent interest. Simulation studies illustrate finite sample properties
of our procedure.
The Dialog State Tracking Challenge with Bayesian Approach
Authors: Quan Nguyen
Date: 2017-02-20 00:00:00
Tasks: dialog state tracking

Generative model has been one of the most common approaches for solving the
Dialog State Tracking Problem with the capabilities to model the dialog
hypotheses in an explicit manner. The most important task in such Bayesian
networks models is constructing the most reliable user models by learning and
reflecting the training data into the probability distribution of user actions
conditional on networks states. This paper provides an overall picture of the
learning process in a Bayesian framework with an emphasize on the
state-of-the-art theoretical analyses of the Expectation Maximization learning
algorithm.
Structured signal recovery from quadratic measurements: Breaking sample complexity barriers via nonconvex optimization
Authors: Mahdi Soltanolkotabi
Date: 2017-02-20 00:00:00
Tasks: Image Reconstruction

This paper concerns the problem of recovering an unknown but structured
signal $x \in R^n$ from $m$ quadratic measurements of the form
$y_r=|<a_r,x>|^2$ for $r=1,2,...,m$. We focus on the under-determined setting
where the number of measurements is significantly smaller than the dimension of
the signal ($m<<n$). We formulate the recovery problem as a nonconvex
optimization problem where prior structural information about the signal is
enforced through constrains on the optimization variables. We prove that
projected gradient descent, when initialized in a neighborhood of the desired
signal, converges to the unknown signal at a linear rate. These results hold
for any constraint set (convex or nonconvex) providing convergence guarantees
to the global optimum even when the objective function and constraint set is
nonconvex. Furthermore, these results hold with a number of measurements that
is only a constant factor away from the minimal number of measurements required
to uniquely identify the unknown signal. Our results provide the first provably
tractable algorithm for this data-poor regime, breaking local sample complexity
barriers that have emerged in recent literature. In a companion paper we
demonstrate favorable properties for the optimization problem that may enable
similar results to continue to hold more globally (over the entire ambient
space). Collectively these two papers utilize and develop powerful tools for
uniform convergence of empirical processes that may have broader implications
for rigorous understanding of constrained nonconvex optimization heuristics.
The mathematical results in this paper also pave the way for a new generation
of data-driven phase-less imaging systems that can utilize prior information to
significantly reduce acquisition time and enhance image reconstruction,
enabling nano-scale imaging at unprecedented speeds and resolutions.
On the Sample Complexity of Learning Graphical Games
Authors: Jean Honorio
Date: 2016-01-27 00:00:00

We analyze the sample complexity of learning graphical games from purely
behavioral data. We assume that we can only observe the players' joint actions
and not their payoffs. We analyze the sufficient and necessary number of
samples for the correct recovery of the set of pure-strategy Nash equilibria
(PSNE) of the true game. Our analysis focuses on directed graphs with $n$ nodes
and at most $k$ parents per node. Sparse graphs correspond to ${k \in O(1)}$
with respect to $n$, while dense graphs correspond to ${k \in O(n)}$. By using
VC dimension arguments, we show that if the number of samples is greater than
${O(k n \log^2{n})}$ for sparse graphs or ${O(n^2 \log{n})}$ for dense graphs,
then maximum likelihood estimation correctly recovers the PSNE with high
probability. By using information-theoretic arguments, we show that if the
number of samples is less than ${\Omega(k n \log^2{n})}$ for sparse graphs or
${\Omega(n^2 \log{n})}$ for dense graphs, then any conceivable method fails to
recover the PSNE with arbitrary probability.
Bayesian Neural Word Embedding
Authors: Oren Barkan
Date: 2016-03-21 00:00:00

Recently, several works in the domain of natural language processing
presented successful methods for word embedding. Among them, the Skip-Gram with
negative sampling, known also as word2vec, advanced the state-of-the-art of
various linguistics tasks. In this paper, we propose a scalable Bayesian neural
word embedding algorithm. The algorithm relies on a Variational Bayes solution
for the Skip-Gram objective and a detailed step by step description is
provided. We present experimental results that demonstrate the performance of
the proposed algorithm for word analogy and similarity tasks on six different
datasets and show it is competitive with the original Skip-Gram method.
Item2Vec: Neural Item Embedding for Collaborative Filtering
Authors: Oren Barkan, Noam Koenigstein
Date: 2016-03-14 00:00:00
Tasks: Collaborative Filtering

Many Collaborative Filtering (CF) algorithms are item-based in the sense that
they analyze item-item relations in order to produce item similarities.
Recently, several works in the field of Natural Language Processing (NLP)
suggested to learn a latent representation of words using neural embedding
algorithms. Among them, the Skip-gram with Negative Sampling (SGNS), also known
as word2vec, was shown to provide state-of-the-art results on various
linguistics tasks. In this paper, we show that item-based CF can be cast in the
same framework of neural word embedding. Inspired by SGNS, we describe a method
we name item2vec for item-based CF that produces embedding for items in a
latent space. The method is capable of inferring item-item relations even when
user information is not available. We present experimental results that
demonstrate the effectiveness of the item2vec method and show it is competitive
with SVD.
Developing a comprehensive framework for multimodal feature extraction
Authors: Quinten McNamara, Alejandro de la Vega, Tal Yarkoni
Date: 2017-02-20 00:00:00

Feature extraction is a critical component of many applied data science
workflows. In recent years, rapid advances in artificial intelligence and
machine learning have led to an explosion of feature extraction tools and
services that allow data scientists to cheaply and effectively annotate their
data along a vast array of dimensions---ranging from detecting faces in images
to analyzing the sentiment expressed in coherent text. Unfortunately, the
proliferation of powerful feature extraction services has been mirrored by a
corresponding expansion in the number of distinct interfaces to feature
extraction services. In a world where nearly every new service has its own API,
documentation, and/or client library, data scientists who need to combine
diverse features obtained from multiple sources are often forced to write and
maintain ever more elaborate feature extraction pipelines. To address this
challenge, we introduce a new open-source framework for comprehensive
multimodal feature extraction. Pliers is an open-source Python package that
supports standardized annotation of diverse data types (video, images, audio,
and text), and is expressly with both ease-of-use and extensibility in mind.
Users can apply a wide range of pre-existing feature extraction tools to their
data in just a few lines of Python code, and can also easily add their own
custom extractors by writing modular classes. A graph-based API enables rapid
development of complex feature extraction pipelines that output results in a
single, standardized format. We describe the package's architecture, detail its
major advantages over previous feature extraction toolboxes, and use a sample
application to a large functional MRI dataset to illustrate how pliers can
significantly reduce the time and effort required to construct sophisticated
feature extraction workflows while increasing code clarity and maintainability.
A Machine Learning Alternative to P-values
Authors: Min Lu, Hemant Ishwaran
Date: 2017-01-18 00:00:00
Tasks: BIG-bench Machine Learning, regression

This paper presents an alternative approach to p-values in regression
settings. This approach, whose origins can be traced to machine learning, is
based on the leave-one-out bootstrap for prediction error. In machine learning
this is called the out-of-bag (OOB) error. To obtain the OOB error for a model,
one draws a bootstrap sample and fits the model to the in-sample data. The
out-of-sample prediction error for the model is obtained by calculating the
prediction error for the model using the out-of-sample data. Repeating and
averaging yields the OOB error, which represents a robust cross-validated
estimate of the accuracy of the underlying model. By a simple modification to
the bootstrap data involving "noising up" a variable, the OOB method yields a
variable importance (VIMP) index, which directly measures how much a specific
variable contributes to the prediction precision of a model. VIMP provides a
scientifically interpretable measure of the effect size of a variable, we call
the "predictive effect size", that holds whether the researcher's model is
correct or not, unlike the p-value whose calculation is based on the assumed
correctness of the model. We also discuss a marginal VIMP index, also easily
calculated, which measures the marginal effect of a variable, or what we call
"the discovery effect". The OOB procedure can be applied to both parametric and
nonparametric regression models and requires only that the researcher can
repeatedly fit their model to bootstrap and modified bootstrap data. We
illustrate this approach on a survival data set involving patients with
systolic heart failure and to a simulated survival data set where the model is
incorrectly specified to illustrate its robustness to model misspecification.
Synthesis versus analysis in patch-based image priors
Authors: Mario A. T. Figueiredo
Date: 2017-02-20 00:00:00
Tasks: Denoising, Image Denoising

In global models/priors (for example, using wavelet frames), there is a well
known analysis vs synthesis dichotomy in the way signal/image priors are
formulated. In patch-based image models/priors, this dichotomy is also present
in the choice of how each patch is modeled. This paper shows that there is
another analysis vs synthesis dichotomy, in terms of how the whole image is
related to the patches, and that all existing patch-based formulations that
provide a global image prior belong to the analysis category. We then propose a
synthesis formulation, where the image is explicitly modeled as being
synthesized by additively combining a collection of independent patches. We
formally establish that these analysis and synthesis formulations are not
equivalent in general and that both formulations are compatible with analysis
and synthesis formulations at the patch level. Finally, we present an instance
of the alternating direction method of multipliers (ADMM) that can be used to
perform image denoising under the proposed synthesis formulation, showing its
computational feasibility. Rather than showing the superiority of the synthesis
or analysis formulations, the contributions of this paper is to establish the
existence of both alternatives, thus closing the corresponding gap in the field
of patch-based image processing.
Computing Human-Understandable Strategies
Authors: Sam Ganzfried, Farzana Yusuf
Date: 2016-12-19 00:00:00

Algorithms for equilibrium computation generally make no attempt to ensure
that the computed strategies are understandable by humans. For instance the
strategies for the strongest poker agents are represented as massive binary
files. In many situations, we would like to compute strategies that can
actually be implemented by humans, who may have computational limitations and
may only be able to remember a small number of features or components of the
strategies that have been computed. We study poker games where private
information distributions can be arbitrary. We create a large training set of
game instances and solutions, by randomly selecting the information
probabilities, and present algorithms that learn from the training instances in
order to perform well in games with unseen information distributions. We are
able to conclude several new fundamental rules about poker strategy that can be
easily implemented by humans.
RESPARC: A Reconfigurable and Energy-Efficient Architecture with Memristive Crossbars for Deep Spiking Neural Networks
Authors: Aayush Ankit, Abhronil Sengupta, Priyadarshini Panda, Kaushik Roy
Date: 2017-02-20 00:00:00
Tasks: 2D Object Detection, 2k

Neuromorphic computing using post-CMOS technologies is gaining immense
popularity due to its promising abilities to address the memory and power
bottlenecks in von-Neumann computing systems. In this paper, we propose RESPARC
- a reconfigurable and energy efficient architecture built-on Memristive
Crossbar Arrays (MCA) for deep Spiking Neural Networks (SNNs). Prior works were
primarily focused on device and circuit implementations of SNNs on crossbars.
RESPARC advances this by proposing a complete system for SNN acceleration and
its subsequent analysis. RESPARC utilizes the energy-efficiency of MCAs for
inner-product computation and realizes a hierarchical reconfigurable design to
incorporate the data-flow patterns in an SNN in a scalable fashion. We evaluate
the proposed architecture on different SNNs ranging in complexity from 2k-230k
neurons and 1.2M-5.5M synapses. Simulation results on these networks show that
compared to the baseline digital CMOS architecture, RESPARC achieves 500X (15X)
efficiency in energy benefits at 300X (60X) higher throughput for multi-layer
perceptrons (deep convolutional networks). Furthermore, RESPARC is a
technology-aware architecture that maps a given SNN topology to the most
optimized MCA size for the given crossbar technology.
Learning to Repeat: Fine Grained Action Repetition for Deep Reinforcement Learning
Authors: Sahil Sharma, Aravind Srinivas, Balaraman Ravindran
Date: 2017-02-20 00:00:00
Tasks: Car Racing, Decision Making, Deep Reinforcement Learning, MuJoCo, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Reinforcement Learning algorithms can learn complex behavioral patterns for sequential decision making tasks wherein an agent interacts with an environment and acquires feedback in the form of rewards sampled from it. Traditionally, such algorithms make decisions, i.e., select actions to execute, at every single time step of the agent-environment interactions. In this paper, we propose a novel framework, Fine Grained Action Repetition (FiGAR), which enables the agent to decide the action as well as the time scale of repeating it. FiGAR can be used for improving any Deep Reinforcement Learning algorithm which maintains an explicit policy estimate by enabling temporal abstractions in the action space. We empirically demonstrate the efficacy of our framework by showing performance improvements on top of three policy search algorithms in different domains: Asynchronous Advantage Actor Critic in the Atari 2600 domain, Trust Region Policy Optimization in Mujoco domain and Deep Deterministic Policy Gradients in the TORCS car racing domain.
An Extended Framework for Marginalized Domain Adaptation
Authors: Gabriela Csurka, Boris Chidlovski, Stephane Clinchant, Sophia Michel
Date: 2017-02-20 00:00:00
Tasks: Denoising, Domain Adaptation, General Classification

We propose an extended framework for marginalized domain adaptation, aimed at
addressing unsupervised, supervised and semi-supervised scenarios. We argue
that the denoising principle should be extended to explicitly promote
domain-invariant features as well as help the classification task. Therefore we
propose to jointly learn the data auto-encoders and the target classifiers.
First, in order to make the denoised features domain-invariant, we propose a
domain regularization that may be either a domain prediction loss or a maximum
mean discrepancy between the source and target data. The noise marginalization
in this case is reduced to solving the linear matrix system $AX=B$ which has a
closed-form solution. Second, in order to help the classification, we include a
class regularization term. Adding this component reduces the learning problem
to solving a Sylvester linear matrix equation $AX+BX=C$, for which an efficient
iterative procedure exists as well. We did an extensive study to assess how
these regularization terms improve the baseline performance in the three domain
adaptation scenarios and present experimental results on two image and one text
benchmark datasets, conventionally used for validating domain adaptation
methods. We report our findings and comparison with state-of-the-art methods.