pwc-archive/papers-with-abstracts huggingface dataset:
A Tractable Fully Bayesian Method for the Stochastic Block Model
Authors: Kohei Hayashi, Takuya Konishi, Tatsuro Kawamoto
Date: 2016-02-06 00:00:00
Tasks: Bayesian Inference, Model Selection, Stochastic Block Model

The stochastic block model (SBM) is a generative model revealing macroscopic
structures in graphs. Bayesian methods are used for (i) cluster assignment
inference and (ii) model selection for the number of clusters. In this paper,
we study the behavior of Bayesian inference in the SBM in the large sample
limit. Combining variational approximation and Laplace's method, a consistent
criterion of the fully marginalized log-likelihood is established. Based on
that, we derive a tractable algorithm that solves tasks (i) and (ii)
concurrently, obviating the need for an outer loop to check all model
candidates. Our empirical and theoretical results demonstrate that our method
is scalable in computation, accurate in approximation, and concise in model
selection.
Reducing training requirements through evolutionary based dimension reduction and subject transfer
Authors: Adham Atyabi, Martin Luerssena, Sean P. Fitzgibbon, Trent Lewis, David M. W. Powersa
Date: 2016-02-06 00:00:00
Tasks: Brain Computer Interface, Dimensionality Reduction, EEG, Electroencephalogram (EEG), Subject Transfer

Training Brain Computer Interface (BCI) systems to understand the intention
of a subject through Electroencephalogram (EEG) data currently requires
multiple training sessions with a subject in order to develop the necessary
expertise to distinguish signals for different tasks. Conventionally the task
of training the subject is done by introducing a training and calibration stage
during which some feedback is presented to the subject. This training session
can take several hours which is not appropriate for on-line EEG-based BCI
systems. An alternative approach is to use previous recording sessions of the
same person or some other subjects that performed the same tasks (subject
transfer) for training the classifiers. The main aim of this study is to
generate a methodology that allows the use of data from other subjects while
reducing the dimensions of the data. The study investigates several
possibilities for reducing the necessary training and calibration period in
subjects and the classifiers and addresses the impact of i) evolutionary
subject transfer and ii) adapting previously trained methods (retraining) using
other subjects data. Our results suggest reduction to 40% of target subject
data is sufficient for training the classifier. Our results also indicate the
superiority of the approaches that incorporated evolutionary subject transfer
and highlights the feasibility of adapting a system trained on other subjects.
Swivel: Improving Embeddings by Noticing What's Missing
Authors: Noam Shazeer, Ryan Doherty, Colin Evans, Chris Waterson
Date: 2016-02-06 00:00:00

We present Submatrix-wise Vector Embedding Learner (Swivel), a method for
generating low-dimensional feature embeddings from a feature co-occurrence
matrix. Swivel performs approximate factorization of the point-wise mutual
information matrix via stochastic gradient descent. It uses a piecewise loss
with special handling for unobserved co-occurrences, and thus makes use of all
the information in the matrix. While this requires computation proportional to
the size of the entire matrix, we make use of vectorized multiplication to
process thousands of rows and columns at once to compute millions of predicted
values. Furthermore, we partition the matrix into shards in order to
parallelize the computation across many nodes. This approach results in more
accurate embeddings than can be achieved with methods that consider only
observed co-occurrences, and can scale to much larger corpora than can be
handled with sampling methods.
Fuzzy Maximum Satisfiability
Authors: Mohamed El Halaby, Areeg Abdalla
Date: 2016-02-06 00:00:00

In this paper, we extend the Maximum Satisfiability (MaxSAT) problem to
{\L}ukasiewicz logic. The MaxSAT problem for a set of formulae {\Phi} is the
problem of finding an assignment to the variables in {\Phi} that satisfies the
maximum number of formulae. Three possible solutions (encodings) are proposed
to the new problem: (1) Disjunctive Linear Relations (DLRs), (2) Mixed Integer
Linear Programming (MILP) and (3) Weighted Constraint Satisfaction Problem
(WCSP). Like its Boolean counterpart, the extended fuzzy MaxSAT will have
numerous applications in optimization problems that involve vagueness.
Classification accuracy as a proxy for two sample testing
Authors: Ilmun Kim, Aaditya Ramdas, Aarti Singh, Larry Wasserman
Date: 2016-02-06 00:00:00
Tasks: Classification, General Classification, Two-sample testing, Vocal Bursts Valence Prediction

When data analysts train a classifier and check if its accuracy is significantly different from chance, they are implicitly performing a two-sample test. We investigate the statistical properties of this flexible approach in the high-dimensional setting. We prove two results that hold for all classifiers in any dimensions: if its true error remains $\epsilon$-better than chance for some $\epsilon>0$ as $d,n \to \infty$, then (a) the permutation-based test is consistent (has power approaching to one), (b) a computationally efficient test based on a Gaussian approximation of the null distribution is also consistent. To get a finer understanding of the rates of consistency, we study a specialized setting of distinguishing Gaussians with mean-difference $\delta$ and common (known or unknown) covariance $\Sigma$, when $d/n \to c \in (0,\infty)$. We study variants of Fisher's linear discriminant analysis (LDA) such as "naive Bayes" in a nontrivial regime when $\epsilon \to 0$ (the Bayes classifier has true accuracy approaching 1/2), and contrast their power with corresponding variants of Hotelling's test. Surprisingly, the expressions for their power match exactly in terms of $n,d,\delta,\Sigma$, and the LDA approach is only worse by a constant factor, achieving an asymptotic relative efficiency (ARE) of $1/\sqrt{\pi}$ for balanced samples. We also extend our results to high-dimensional elliptical distributions with finite kurtosis. Other results of independent interest include minimax lower bounds, and the optimality of Hotelling's test when $d=o(n)$. Simulation results validate our theory, and we present practical takeaway messages along with natural open problems.
BISTRO: An Efficient Relaxation-Based Method for Contextual Bandits
Authors: Alexander Rakhlin, Karthik Sridharan
Date: 2016-02-06 00:00:00
Tasks: Multi-Armed Bandits

We present efficient algorithms for the problem of contextual bandits with
i.i.d. covariates, an arbitrary sequence of rewards, and an arbitrary class of
policies. Our algorithm BISTRO requires d calls to the empirical risk
minimization (ERM) oracle per round, where d is the number of actions. The
method uses unlabeled data to make the problem computationally simple. When the
ERM problem itself is computationally hard, we extend the approach by employing
multiplicative approximation algorithms for the ERM. The integrality gap of the
relaxation only enters in the regret bound rather than the benchmark. Finally,
we show that the adversarial version of the contextual bandit problem is
learnable (and efficient) whenever the full-information supervised online
learning problem has a non-trivial regret guarantee (and efficient).
Active Information Acquisition
Authors: He He, Paul Mineiro, Nikos Karampatziakis
Date: 2016-02-05 00:00:00
Tasks: General Reinforcement Learning, Reinforcement Learning, Reinforcement Learning (RL), Sentiment Analysis

We propose a general framework for sequential and dynamic acquisition of
useful information in order to solve a particular task. While our goal could in
principle be tackled by general reinforcement learning, our particular setting
is constrained enough to allow more efficient algorithms. In this paper, we
work under the Learning to Search framework and show how to formulate the goal
of finding a dynamic information acquisition policy in that framework. We apply
our formulation on two tasks, sentiment analysis and image recognition, and
show that the learned policies exhibit good statistical performance. As an
emergent byproduct, the learned policies show a tendency to focus on the most
prominent parts of each instance and give harder instances more attention
without explicitly being trained to do so.
On Column Selection in Approximate Kernel Canonical Correlation Analysis
Authors: Weiran Wang
Date: 2016-02-05 00:00:00
Tasks: Model Selection

We study the problem of column selection in large-scale kernel canonical
correlation analysis (KCCA) using the Nystr\"om approximation, where one
approximates two positive semi-definite kernel matrices using "landmark" points
from the training set. When building low-rank kernel approximations in KCCA,
previous work mostly samples the landmarks uniformly at random from the
training set. We propose novel strategies for sampling the landmarks
non-uniformly based on a version of statistical leverage scores recently
developed for kernel ridge regression. We study the approximation accuracy of
the proposed non-uniform sampling strategy, develop an incremental algorithm
that explores the path of approximation ranks and facilitates efficient model
selection, and derive the kernel stability of out-of-sample mapping for our
method. Experimental results on both synthetic and real-world datasets
demonstrate the promise of our method.
Probabilistic Extension to the Concurrent Constraint Factor Oracle Model for Music Improvisation
Authors: Mauricio Toro
Date: 2016-02-05 00:00:00

We can program a Real-Time (RT) music improvisation system in C++ without a
formal semantic or we can model it with process calculi such as the
Non-deterministic Timed Concurrent Constraint (ntcc) calculus. "A Concurrent
Constraints Factor Oracle (FO) model for Music Improvisation" (Ccfomi) is an
improvisation model specified on ntcc. Since Ccfomi improvises
non-deterministically, there is no control on choices and therefore little
control over the sequence variation during the improvisation. To avoid this, we
extended Ccfomi using the Probabilistic Non-deterministic Timed Concurrent
Constraint calculus. Our extension to Ccfomi does not change the time and space
complexity of building the FO, thus making our extension compatible with RT.
However, there was not a ntcc interpreter capable of RT to execute Ccfomi. We
developed Ntccrt --a RT capable interpreter for ntcc-- and we executed Ccfomi
on Ntccrt. In the future, we plan to extend Ntccrt to execute our extension to
Ccfomi.
A Note on Alternating Minimization Algorithm for the Matrix Completion Problem
Authors: David Gamarnik, Sidhant Misra
Date: 2016-02-05 00:00:00
Tasks: Matrix Completion

We consider the problem of reconstructing a low rank matrix from a subset of
its entries and analyze two variants of the so-called Alternating Minimization
algorithm, which has been proposed in the past. We establish that when the
underlying matrix has rank $r=1$, has positive bounded entries, and the graph
$\mathcal{G}$ underlying the revealed entries has bounded degree and diameter
which is at most logarithmic in the size of the matrix, both algorithms succeed
in reconstructing the matrix approximately in polynomial time starting from an
arbitrary initialization. We further provide simulation results which suggest
that the second algorithm which is based on the message passing type updates,
performs significantly better.
Daleel: Simplifying Cloud Instance Selection Using Machine Learning
Authors: Faiza Samreen, Yehia Elkhatib, Matthew Rowe, Gordon S. Blair
Date: 2016-02-05 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making, Diversity

Decision making in cloud environments is quite challenging due to the
diversity in service offerings and pricing models, especially considering that
the cloud market is an incredibly fast moving one. In addition, there are no
hard and fast rules, each customer has a specific set of constraints (e.g.
budget) and application requirements (e.g. minimum computational resources).
Machine learning can help address some of the complicated decisions by carrying
out customer-specific analytics to determine the most suitable instance type(s)
and the most opportune time for starting or migrating instances. We employ
machine learning techniques to develop an adaptive deployment policy, providing
an optimal match between the customer demands and the available cloud service
offerings. We provide an experimental study based on extensive set of job
executions over a major public cloud infrastructure.
Reducing Runtime by Recycling Samples
Authors: Jialei Wang, Hai Wang, Nathan Srebro
Date: 2016-02-05 00:00:00

Contrary to the situation with stochastic gradient descent, we argue that
when using stochastic methods with variance reduction, such as SDCA, SAG or
SVRG, as well as their variants, it could be beneficial to reuse previously
used samples instead of fresh samples, even when fresh samples are available.
We demonstrate this empirically for SDCA, SAG and SVRG, studying the optimal
sample size one should use, and also uncover be-havior that suggests running
SDCA for an integer number of epochs could be wasteful.
Mining Software Quality from Software Reviews: Research Trends and Open Issues
Authors: Issa Atoum, Ahmed Otoom
Date: 2016-02-05 00:00:00
Tasks: Opinion Mining, Sentiment Analysis

Software review text fragments have considerably valuable information about
users experience. It includes a huge set of properties including the software
quality. Opinion mining or sentiment analysis is concerned with analyzing
textual user judgments. The application of sentiment analysis on software
reviews can find a quantitative value that represents software quality.
Although many software quality methods are proposed they are considered
difficult to customize and many of them are limited. This article investigates
the application of opinion mining as an approach to extract software quality
properties. We found that the major issues of software reviews mining using
sentiment analysis are due to software lifecycle and the diverse users and
teams.
Sub-cortical brain structure segmentation using F-CNN's
Authors: Mahsa Shakeri, Stavros Tsogkas, Enzo Ferrante, Sarah Lippe, Samuel Kadoury, Nikos Paragios, Iasonas Kokkinos
Date: 2016-02-05 00:00:00
Tasks: Segmentation, Semantic Segmentation

In this paper we propose a deep learning approach for segmenting sub-cortical
structures of the human brain in Magnetic Resonance (MR) image data. We draw
inspiration from a state-of-the-art Fully-Convolutional Neural Network (F-CNN)
architecture for semantic segmentation of objects in natural images, and adapt
it to our task. Unlike previous CNN-based methods that operate on image
patches, our model is applied on a full blown 2D image, without any alignment
or registration steps at testing time. We further improve segmentation results
by interpreting the CNN output as potentials of a Markov Random Field (MRF),
whose topology corresponds to a volumetric grid. Alpha-expansion is used to
perform approximate inference imposing spatial volumetric homogeneity to the
CNN priors. We compare the performance of the proposed pipeline with a similar
system using Random Forest-based priors, as well as state-of-art segmentation
algorithms, and show promising results on two different brain MRI datasets.
Sequence Classification with Neural Conditional Random Fields
Authors: Myriam Abramson
Date: 2016-02-05 00:00:00
Tasks: Classification, General Classification, Part-Of-Speech Tagging, Prediction, Sensor Fusion, Structured Prediction, TAG, Temporal Sequences

The proliferation of sensor devices monitoring human activity generates
voluminous amount of temporal sequences needing to be interpreted and
categorized. Moreover, complex behavior detection requires the personalization
of multi-sensor fusion algorithms. Conditional random fields (CRFs) are
commonly used in structured prediction tasks such as part-of-speech tagging in
natural language processing. Conditional probabilities guide the choice of each
tag/label in the sequence conflating the structured prediction task with the
sequence classification task where different models provide different
categorization of the same sequence. The claim of this paper is that CRF models
also provide discriminative models to distinguish between types of sequence
regardless of the accuracy of the labels obtained if we calibrate the class
membership estimate of the sequence. We introduce and compare different neural
network based linear-chain CRFs and we present experiments on two complex
sequence classification and structured prediction tasks to support this claim.
How scientific literature has been evolving over the time? A novel statistical approach using tracking verbal-based methods
Authors: Daria Micaela Hernandez, Monica Becue-Bertaut, Igor Barahona
Date: 2016-02-05 00:00:00
Tasks: Articles

This paper provides a global vision of the scientific publications related
with the Systemic Lupus Erythematosus (SLE), taking as starting point abstracts
of articles. Through the time, abstracts have been evolving towards higher
complexity on used terminology, which makes necessary the use of sophisticated
statistical methods and answering questions including: how vocabulary is
evolving through the time? Which ones are most influential articles? And which
one are the articles that introduced new terms and vocabulary? To answer these,
we analyze a dataset composed by 506 abstracts and downloaded from 115
different journals and cover a 18 year-period.
Harmonic Grammar in a DisCo Model of Meaning
Authors: Martha Lewis, Bob Coecke
Date: 2016-02-05 00:00:00

The model of cognition developed in (Smolensky and Legendre, 2006) seeks to
unify two levels of description of the cognitive process: the connectionist and
the symbolic. The theory developed brings together these two levels into the
Integrated Connectionist/Symbolic Cognitive architecture (ICS). Clark and
Pulman (2007) draw a parallel with semantics where meaning may be modelled on
both distributional and symbolic levels, developed by Coecke et al, 2010 into
the Distributional Compositional (DisCo) model of meaning. In the current work,
we revisit Smolensky and Legendre (S&L)'s model. We describe the DisCo
framework, summarise the key ideas in S&L's architecture, and describe how
their description of harmony as a graded measure of grammaticality may be
applied in the DisCo model.
Region Based Approximation for High Dimensional Bayesian Network Models
Authors: Peng Lin, Martin Neil, Norman Fenton
Date: 2016-02-05 00:00:00
Tasks: Clustering, Triplet, Vocal Bursts Intensity Prediction

Performing efficient inference on Bayesian Networks (BNs), with large numbers
of densely connected variables is challenging. With exact inference methods,
such as the Junction Tree algorithm, clustering complexity can grow
exponentially with the number of nodes and so computation becomes intractable.
This paper presents a general purpose approximate inference algorithm called
Triplet Region Construction (TRC) that reduces the clustering complexity for
factorized models from worst case exponential to polynomial. We employ graph
factorization to reduce connection complexity and produce clusters of limited
size. Unlike MCMC algorithms TRC is guaranteed to converge and we present
experiments that show that TRC achieves accurate results when compared with
exact solutions.
Utilização de Grafos e Matriz de Similaridade na Sumarização Automática de Documentos Baseada em Extração de Frases
Authors: Elvys Linhares Pontes
Date: 2016-02-05 00:00:00
Tasks: Text Summarization

The internet increased the amount of information available. However, the
reading and understanding of this information are costly tasks. In this
scenario, the Natural Language Processing (NLP) applications enable very
important solutions, highlighting the Automatic Text Summarization (ATS), which
produce a summary from one or more source texts. Automatically summarizing one
or more texts, however, is a complex task because of the difficulties inherent
to the analysis and generation of this summary. This master's thesis describes
the main techniques and methodologies (NLP and heuristics) to generate
summaries. We have also addressed and proposed some heuristics based on graphs
and similarity matrix to measure the relevance of judgments and to generate
summaries by extracting sentences. We used the multiple languages (English,
French and Spanish), CSTNews (Brazilian Portuguese), RPM (French) and DECODA
(French) corpus to evaluate the developped systems. The results obtained were
quite interesting.
Strategic disclosure of opinions on a social network
Authors: Umberto Grandi, Emiliano Lorini, Laurent Perrussel
Date: 2016-02-05 00:00:00

We study the strategic aspects of social influence in a society of agents
linked by a trust network, introducing a new class of games called games of
influence. A game of influence is an infinite repeated game with incomplete
information in which, at each stage of interaction, an agent can make her
opinions visible (public) or invisible (private) in order to influence other
agents' opinions. The influence process is mediated by a trust network, as we
assume that the opinion of a given agent is only affected by the opinions of
those agents that she considers trustworthy (i.e., the agents in the trust
network that are directly linked to her). Each agent is endowed with a goal,
expressed in a suitable temporal language inspired from linear temporal logic
(LTL). We show that games of influence provide a simple abstraction to explore
the effects of the trust network structure on the agents' behaviour, by
considering solution concepts from game-theory such as Nash equilibrium, weak
dominance and winning strategies.
Efficient Multi-view Performance Capture of Fine-Scale Surface Detail
Authors: Nadia Robertini, Edilson de Aguiar, Thomas Helten, Christian Theobalt
Date: 2016-02-05 00:00:00
Tasks: Occlusion Handling

We present a new effective way for performance capture of deforming meshes
with fine-scale time-varying surface detail from multi-view video. Our method
builds up on coarse 4D surface reconstructions, as obtained with commonly used
template-based methods. As they only capture models of coarse-to-medium scale
detail, fine scale deformation detail is often done in a second pass by using
stereo constraints, features, or shading-based refinement. In this paper, we
propose a new effective and stable solution to this second step. Our framework
creates an implicit representation of the deformable mesh using a dense
collection of 3D Gaussian functions on the surface, and a set of 2D Gaussians
for the images. The fine scale deformation of all mesh vertices that maximizes
photo-consistency can be efficiently found by densely optimizing a new
model-to-image consistency energy on all vertex positions. A principal
advantage is that our problem formulation yields a smooth closed form energy
with implicit occlusion handling and analytic derivatives. Error-prone
correspondence finding, or discrete sampling of surface displacement values are
also not needed. We show several reconstructions of human subjects wearing
loose clothing, and we qualitatively and quantitatively show that we robustly
capture more detail than related methods.