pwc-archive/papers-with-abstracts huggingface dataset:
Policy Error Bounds for Model-Based Reinforcement Learning with Factored Linear Models
Authors: Bernardo Ávila Pires, Csaba Szepesvári
Date: 2016-02-19 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper we study a model-based approach to calculating approximately
optimal policies in Markovian Decision Processes. In particular, we derive
novel bounds on the loss of using a policy derived from a factored linear
model, a class of models which generalize numerous previous models out of those
that come with strong computational guarantees. For the first time in the
literature, we derive performance bounds for model-based techniques where the
model inaccuracy is measured in weighted norms. Moreover, our bounds show a
decreased sensitivity to the discount factor and, unlike similar bounds derived
for other approaches, they are insensitive to measure mismatch. Similarly to
previous works, our proofs are also based on contraction arguments, but with
the main differences that we use carefully constructed norms building on Banach
lattices, and the contraction property is only assumed for operators acting on
"compressed" spaces, thus weakening previous assumptions, while strengthening
previous results.
Recognizing Implicit Discourse Relations via Repeated Reading: Neural Networks with Multi-Level Attention
Authors: Yang Liu, Sujian Li
Date: 2016-09-20 00:00:00
Proceeding: EMNLP 2016 11

Recognizing implicit discourse relations is a challenging but important task
in the field of Natural Language Processing. For such a complex text processing
task, different from previous studies, we argue that it is necessary to
repeatedly read the arguments and dynamically exploit the efficient features
useful for recognizing discourse relations. To mimic the repeated reading
strategy, we propose the neural networks with multi-level attention (NNMA),
combining the attention mechanism and external memories to gradually fix the
attention on some specific words helpful to judging the discourse relations.
Experiments on the PDTB dataset show that our proposed method achieves the
state-of-art results. The visualization of the attention weights also
illustrates the progress that our model observes the arguments on each level
and progressively locates the important words.
A Theory of Interactive Debugging of Knowledge Bases in Monotonic Logics
Authors: Patrick Rodler
Date: 2016-09-20 00:00:00

A broad variety of knowledge-based applications such as recommender, expert,
planning or configuration systems usually operate on the basis of knowledge
represented by means of some logical language. Such a logical knowledge base
(KB) enables intelligent behavior of such systems by allowing them to
automatically reason, answer queries of interest or solve complex real-world
problems. Nowadays, where information acquisition comes at low costs and often
happens automatically, the applied KBs are continuously growing in terms of
size, information content and complexity. These developments foster the
emergence of errors in these KBs and thus pose a significant challenge on all
people and tools involved in KB evolution, maintenance and application.
  If some minimal quality criteria such as logical consistency are not met by
some KB, it becomes useless for knowledge-based applications. To guarantee the
compliance of KBs with given requirements, (non-interactive) KB debuggers have
been proposed. These however often cannot localize all potential faults,
suggest too large or incorrect modifications of the faulty KB or suffer from
poor scalability due to the inherent complexity of the KB debugging problem.
  As a remedy to these issues, based on a well-founded theoretical basis this
work proposes complete, sound and optimal methods for the interactive debugging
of KBs that suggest the one (minimally invasive) error correction of the faulty
KB that yields a repaired KB with exactly the intended semantics. Users, e.g.
domain experts, are involved in the debugging process by answering
automatically generated queries whether some given statements must or must not
hold in the domain that should be modeled by the problematic KB at hand.
Automatic Sarcasm Detection: A Survey
Authors: Aditya Joshi, Pushpak Bhattacharyya, Mark James Carman
Date: 2016-02-10 00:00:00
Tasks: Sarcasm Detection, Sentiment Analysis, Survey

Automatic sarcasm detection is the task of predicting sarcasm in text. This
is a crucial step to sentiment analysis, considering prevalence and challenges
of sarcasm in sentiment-bearing text. Beginning with an approach that used
speech-based features, sarcasm detection has witnessed great interest from the
sentiment analysis community. This paper is the first known compilation of past
work in automatic sarcasm detection. We observe three milestones in the
research so far: semi-supervised pattern extraction to identify implicit
sentiment, use of hashtag-based supervision, and use of context beyond target
text. In this paper, we describe datasets, approaches, trends and issues in
sarcasm detection. We also discuss representative performance values, shared
tasks and pointers to future work, as given in prior works. In terms of
resources that could be useful for understanding state-of-the-art, the survey
presents several useful illustrations - most prominently, a table that
summarizes past papers along different dimensions such as features, annotation
techniques, data forms, etc.
Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests
Authors: Narges Razavian, Jake Marcus, David Sontag
Date: 2016-08-02 00:00:00

Disparate areas of machine learning have benefited from models that can take
raw data with little preprocessing as input and learn rich representations of
that raw data in order to perform well on a given prediction task. We evaluate
this approach in healthcare by using longitudinal measurements of lab tests,
one of the more raw signals of a patient's health state widely available in
clinical data, to predict disease onsets. In particular, we train a Long
Short-Term Memory (LSTM) recurrent neural network and two novel convolutional
neural networks for multi-task prediction of disease onset for 133 conditions
based on 18 common lab tests measured over time in a cohort of 298K patients
derived from 8 years of administrative claims data. We compare the neural
networks to a logistic regression with several hand-engineered, clinically
relevant features. We find that the representation-based learning approaches
significantly outperform this baseline. We believe that our work suggests a new
avenue for patient risk stratification based solely on lab results.
Unified Statistical Theory of Spectral Graph Analysis
Authors: Subhadeep Mukhopadhyay
Date: 2016-02-11 00:00:00

The goal of this paper is to show that there exists a simple, yet universal
statistical logic of spectral graph analysis by recasting it into a
nonparametric function estimation problem. The prescribed viewpoint appears to
be good enough to accommodate most of the existing spectral graph techniques as
a consequence of just one single formalism and algorithm.
Long Short-Term Memory-Networks for Machine Reading
Authors: Jianpeng Cheng, Li Dong, Mirella Lapata
Date: 2016-01-25 00:00:00
Proceeding: EMNLP 2016 11
Tasks: Decoder, Language Modeling, Language Modelling, Natural Language Inference, Reading Comprehension, Sentiment Analysis

In this paper we address the question of how to render sequence-level
networks better at handling structured input. We propose a machine reading
simulator which processes text incrementally from left to right and performs
shallow reasoning with memory and attention. The reader extends the Long
Short-Term Memory architecture with a memory network in place of a single
memory cell. This enables adaptive memory usage during recurrence with neural
attention, offering a way to weakly induce relations among tokens. The system
is initially designed to process a single sequence but we also demonstrate how
to integrate it with an encoder-decoder architecture. Experiments on language
modeling, sentiment analysis, and natural language inference show that our
model matches or outperforms the state of the art.
Markov Random Field Model-Based Salt and Pepper Noise Removal
Authors: Ahmadreza Baghaie
Date: 2016-09-20 00:00:00
Tasks: Salt-And-Pepper Noise Removal, SSIM

Problem of impulse noise reduction is a very well studied problem in image
processing community and many different approaches have been proposed to tackle
this problem. In the current work, the problem of fixed value impulse noise
(salt and pepper) removal from images is investigated by use of a Markov Random
Field (MRF) models with smoothness priors. After the formulation of the problem
as an inpainting problem, graph cuts with $\alpha$-expansion moves are
considered for minimization of the energy functional. As for comparisons,
several other minimization techniques that are widely used for MRF models'
optimization are considered and the results are compared using
Peak-Signal-to-Noise-Ratio (PSNR) and Structural Similarity Index (SSIM) as
metrics. The investigations show the superiority of graph cuts with
$\alpha$-expansion moves over the other techniques both in terms of PSNR and
also computational times.
Unsupervised learning of transcriptional regulatory networks via latent tree graphical models
Authors: Anthony Gitter, Furong Huang, Ragupathyraj Valluvan, Ernest Fraenkel, Animashree Anandkumar
Date: 2016-09-20 00:00:00

Gene expression is a readily-observed quantification of transcriptional
activity and cellular state that enables the recovery of the relationships
between regulators and their target genes. Reconstructing transcriptional
regulatory networks from gene expression data is a problem that has attracted
much attention, but previous work often makes the simplifying (but unrealistic)
assumption that regulator activity is represented by mRNA levels. We use a
latent tree graphical model to analyze gene expression without relying on
transcription factor expression as a proxy for regulator activity. The latent
tree model is a type of Markov random field that includes both observed gene
variables and latent (hidden) variables, which factorize on a Markov tree.
Through efficient unsupervised learning approaches, we determine which groups
of genes are co-regulated by hidden regulators and the activity levels of those
regulators. Post-processing annotates many of these discovered latent variables
as specific transcription factors or groups of transcription factors. Other
latent variables do not necessarily represent physical regulators but instead
reveal hidden structure in the gene expression such as shared biological
function. We apply the latent tree graphical model to a yeast stress response
dataset. In addition to novel predictions, such as condition-specific binding
of the transcription factor Msn4, our model recovers many known aspects of the
yeast regulatory network. These include groups of co-regulated genes,
condition-specific regulator activity, and combinatorial regulation among
transcription factors. The latent tree graphical model is a general approach
for analyzing gene expression data that requires no prior knowledge of which
possible regulators exist, regulator activity, or where transcription factors
physically bind.
Non-convex Global Minimization and False Discovery Rate Control for the TREX
Authors: Jacob Bien, Irina Gaynanova, Johannes Lederer, Christian Müller
Date: 2016-04-22 00:00:00

The TREX is a recently introduced method for performing sparse
high-dimensional regression. Despite its statistical promise as an alternative
to the lasso, square-root lasso, and scaled lasso, the TREX is computationally
challenging in that it requires solving a non-convex optimization problem. This
paper shows a remarkable result: despite the non-convexity of the TREX problem,
there exists a polynomial-time algorithm that is guaranteed to find the global
minimum. This result adds the TREX to a very short list of non-convex
optimization problems that can be globally optimized (principal components
analysis being a famous example). After deriving and developing this new
approach, we demonstrate that (i) the ability of the preexisting TREX heuristic
to reach the global minimum is strongly dependent on the difficulty of the
underlying statistical problem, (ii) the new polynomial-time algorithm for TREX
permits a novel variable ranking and selection scheme, (iii) this scheme can be
incorporated into a rule that controls the false discovery rate (FDR) of
included features in the model. To achieve this last aim, we provide an
extension of the results of Barber & Candes (2015) to establish that the
knockoff filter framework can be applied to the TREX. This investigation thus
provides both a rare case study of a heuristic for non-convex optimization and
a novel way of exploiting non-convexity for statistical inference.
Option Discovery in Hierarchical Reinforcement Learning using Spatio-Temporal Clustering
Authors: Aravind Srinivas, Ramnandan Krishnamurthy, Peeyush Kumar, Balaraman Ravindran
Date: 2016-05-17 00:00:00
Tasks: Clustering, Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Video Prediction

This paper introduces an automated skill acquisition framework in reinforcement learning which involves identifying a hierarchical description of the given task in terms of abstract states and extended actions between abstract states. Identifying such structures present in the task provides ways to simplify and speed up reinforcement learning algorithms. These structures also help to generalize such algorithms over multiple tasks without relearning policies from scratch. We use ideas from dynamical systems to find metastable regions in the state space and associate them with abstract states. The spectral clustering algorithm PCCA+ is used to identify suitable abstractions aligned to the underlying structure. Skills are defined in terms of the sequence of actions that lead to transitions between such abstract states. The connectivity information from PCCA+ is used to generate these skills or options. These skills are independent of the learning task and can be efficiently reused across a variety of tasks defined over the same model. This approach works well even without the exact model of the environment by using sample trajectories to construct an approximate estimate. We also present our approach to scaling the skill acquisition framework to complex tasks with large state spaces for which we perform state aggregation using the representation learned from an action conditional video prediction network and use the skill acquisition framework on the aggregated state space.
ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning
Authors: Michał Kempka, Marek Wydmuch, Grzegorz Runc, Jakub Toczek, Wojciech Jaśkowski
Date: 2016-05-06 00:00:00
Tasks: Atari Games, FPS Games, Game of Doom, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The recent advances in deep neural networks have led to effective
vision-based reinforcement learning methods that have been employed to obtain
human-level controllers in Atari 2600 games from pixel data. Atari 2600 games,
however, do not resemble real-world tasks since they involve non-realistic 2D
environments and the third-person perspective. Here, we propose a novel
test-bed platform for reinforcement learning research from raw visual
information which employs the first-person perspective in a semi-realistic 3D
world. The software, called ViZDoom, is based on the classical first-person
shooter video game, Doom. It allows developing bots that play the game using
the screen buffer. ViZDoom is lightweight, fast, and highly customizable via a
convenient mechanism of user scenarios. In the experimental part, we test the
environment by trying to learn bots for two scenarios: a basic move-and-shoot
task and a more complex maze-navigation problem. Using convolutional deep
neural networks with Q-learning and experience replay, for both scenarios, we
were able to train competent bots, which exhibit human-like behaviors. The
results confirm the utility of ViZDoom as an AI research platform and imply
that visual reinforcement learning in 3D realistic first-person perspective
environments is feasible.
Semantic Similarity Strategies for Job Title Classification
Authors: Yun Zhu, Faizan Javed, Ozgur Ozturk
Date: 2016-09-20 00:00:00
Tasks: Classification, General Classification, Semantic Similarity, Semantic Textual Similarity

Automatic and accurate classification of items enables numerous downstream
applications in many domains. These applications can range from faceted
browsing of items to product recommendations and big data analytics. In the
online recruitment domain, we refer to classifying job ads to pre-defined or
custom occupation categories as job title classification. A large-scale job
title classification system can power various downstream applications such as
semantic search, job recommendations and labor market analytics. In this paper,
we discuss experiments conducted to improve our in-house job title
classification system. The classification component of the system is composed
of a two-stage coarse and fine level classifier cascade that classifies input
text such as job title and/or job ads to one of the thousands of job titles in
our taxonomy. To improve classification accuracy and effectiveness, we
experiment with various semantic representation strategies such as average W2V
vectors and document similarity measures such as Word Movers Distance (WMD).
Our initial results show an overall improvement in accuracy of Carotene[1].
The Quadrifocal Variety
Authors: Luke Oeding
Date: 2015-01-06 00:00:00

Multi-view Geometry is reviewed from an Algebraic Geometry perspective and
multi-focal tensors are constructed as equivariant projections of the
Grassmannian. A connection to the principal minor assignment problem is made by
considering several flatlander cameras. The ideal of the quadrifocal variety is
computed up to degree 8 (and partially in degree 9) using the representations
of $\operatorname{GL}(3)^{\times 4}$ in the polynomial ring on the space of $3
\times 3 \times 3 \times 3$ tensors. Further representation-theoretic analysis
gives a lower bound for the number of minimal generators.
GAdaBoost: Accelerating Adaboost Feature Selection with Genetic Algorithms
Authors: Mai Tolba, Mohamed Moustafa
Date: 2016-09-20 00:00:00
Tasks: Face Detection, feature selection, object-detection, Object Detection

Boosted cascade of simple features, by Viola and Jones, is one of the most
famous object detection frameworks. However, it suffers from a lengthy training
process. This is due to the vast features space and the exhaustive search
nature of Adaboost. In this paper we propose GAdaboost: a Genetic Algorithm to
accelerate the training procedure through natural feature selection.
Specifically, we propose to limit Adaboost search within a subset of the huge
feature space, while evolving this subset following a Genetic Algorithm.
Experiments demonstrate that our proposed GAdaboost is up to 3.7 times faster
than Adaboost. We also demonstrate that the price of this speedup is a mere
decrease (3%, 4%) in detection accuracy when tested on FDDB benchmark face
detection set, and Caltech Web Faces respectively.
Relative Error Embeddings for the Gaussian Kernel Distance
Authors: Di Chen, Jeff M. Phillips
Date: 2016-02-17 00:00:00

A reproducing kernel can define an embedding of a data point into an infinite
dimensional reproducing kernel Hilbert space (RKHS). The norm in this space
describes a distance, which we call the kernel distance. The random Fourier
features (of Rahimi and Recht) describe an oblivious approximate mapping into
finite dimensional Euclidean space that behaves similar to the RKHS. We show in
this paper that for the Gaussian kernel the Euclidean norm between these mapped
to features has $(1+\epsilon)$-relative error with respect to the kernel
distance. When there are $n$ data points, we show that $O((1/\epsilon^2)
\log(n))$ dimensions of the approximate feature space are sufficient and
necessary.
  Without a bound on $n$, but when the original points lie in $\mathbb{R}^d$
and have diameter bounded by $\mathcal{M}$, then we show that $O((d/\epsilon^2)
\log(\mathcal{M}))$ dimensions are sufficient, and that this many are required,
up to $\log(1/\epsilon)$ factors.
Coupled Generative Adversarial Networks
Authors: Ming-Yu Liu, Oncel Tuzel
Date: 2016-06-24 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Domain Adaptation, Generative Adversarial Network, Image-to-Image Translation

We propose coupled generative adversarial network (CoGAN) for learning a
joint distribution of multi-domain images. In contrast to the existing
approaches, which require tuples of corresponding images in different domains
in the training set, CoGAN can learn a joint distribution without any tuple of
corresponding images. It can learn a joint distribution with just samples drawn
from the marginal distributions. This is achieved by enforcing a weight-sharing
constraint that limits the network capacity and favors a joint distribution
solution over a product of marginal distributions one. We apply CoGAN to
several joint distribution learning tasks, including learning a joint
distribution of color and depth images, and learning a joint distribution of
face images with different attributes. For each task it successfully learns the
joint distribution without any tuple of corresponding images. We also
demonstrate its applications to domain adaptation and image transformation.
Generating Politically-Relevant Event Data
Authors: John Beieler
Date: 2016-09-20 00:00:00
Proceeding: WS 2016 11

Automatically generated political event data is an important part of the
social science data ecosystem. The approaches for generating this data, though,
have remained largely the same for two decades. During this time, the field of
computational linguistics has progressed tremendously. This paper presents an
overview of political event data, including methods and ontologies, and a set
of experiments to determine the applicability of deep neural networks to the
extraction of political events from news text.
Bayesian Variable Selection for Globally Sparse Probabilistic PCA
Authors: Charles Bouveyron, Pierre Latouche, Pierre-Alexandre Mattei
Date: 2016-05-19 00:00:00
Tasks: Model Selection, Variable Selection

Sparse versions of principal component analysis (PCA) have imposed themselves
as simple, yet powerful ways of selecting relevant features of high-dimensional
data in an unsupervised manner. However, when several sparse principal
components are computed, the interpretation of the selected variables is
difficult since each axis has its own sparsity pattern and has to be
interpreted separately. To overcome this drawback, we propose a Bayesian
procedure called globally sparse probabilistic PCA (GSPPCA) that allows to
obtain several sparse components with the same sparsity pattern. This allows
the practitioner to identify the original variables which are relevant to
describe the data. To this end, using Roweis' probabilistic interpretation of
PCA and a Gaussian prior on the loading matrix, we provide the first exact
computation of the marginal likelihood of a Bayesian PCA model. To avoid the
drawbacks of discrete model selection, a simple relaxation of this framework is
presented. It allows to find a path of models using a variational
expectation-maximization algorithm. The exact marginal likelihood is then
maximized over this path. This approach is illustrated on real and synthetic
data sets. In particular, using unlabeled microarray data, GSPPCA infers much
more relevant gene subsets than traditional sparse PCA algorithms.