pwc-archive/papers-with-abstracts huggingface dataset:
The Effect of Biased Communications On Both Trusting and Suspicious Voters
Authors: William W. Cohen, David P. Redlawsk, Douglas Pierce
Date: 2013-06-11 00:00:00
Tasks: Decision Making

In recent studies of political decision-making, apparently anomalous behavior
has been observed on the part of voters, in which negative information about a
candidate strengthens, rather than weakens, a prior positive opinion about the
candidate. This behavior appears to run counter to rational models of decision
making, and it is sometimes interpreted as evidence of non-rational "motivated
reasoning". We consider scenarios in which this effect arises in a model of
rational decision making which includes the possibility of deceptive
information. In particular, we will consider a model in which there are two
classes of voters, which we will call trusting voters and suspicious voters,
and two types of information sources, which we will call unbiased sources and
biased sources. In our model, new data about a candidate can be efficiently
incorporated by a trusting voter, and anomalous updates are impossible;
however, anomalous updates can be made by suspicious voters, if the information
source mistakenly plans for an audience of trusting voters, and if the partisan
goals of the information source are known by the suspicious voter to be
"opposite" to his own. Our model is based on a formalism introduced by the
artificial intelligence community called "multi-agent influence diagrams",
which generalize Bayesian networks to settings involving multiple agents with
distinct goals.
The association problem in wireless networks: a Policy Gradient Reinforcement Learning approach
Authors: Richard Combes, Ilham El Bouloumi, Stephane Senecal, Zwi Altman
Date: 2013-06-11 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The purpose of this paper is to develop a self-optimized association
algorithm based on PGRL (Policy Gradient Reinforcement Learning), which is both
scalable, stable and robust. The term robust means that performance degradation
in the learning phase should be forbidden or limited to predefined thresholds.
The algorithm is model-free (as opposed to Value Iteration) and robust (as
opposed to Q-Learning). The association problem is modeled as a Markov Decision
Process (MDP). The policy space is parameterized. The parameterized family of
policies is then used as expert knowledge for the PGRL. The PGRL converges
towards a local optimum and the average cost decreases monotonically during the
learning process. The properties of the solution make it a good candidate for
practical implementation. Furthermore, the robustness property allows to use
the PGRL algorithm in an "always-on" learning mode.
Hybrid Maximum Likelihood Modulation Classification Using Multiple Radios
Authors: Onur Ozdemir, Ruoyu Li, Pramod K. Varshney
Date: 2013-03-04 00:00:00
Tasks: Classification, Diversity, General Classification

The performance of a modulation classifier is highly sensitive to channel
signal-to-noise ratio (SNR). In this paper, we focus on amplitude-phase
modulations and propose a modulation classification framework based on
centralized data fusion using multiple radios and the hybrid maximum likelihood
(ML) approach. In order to alleviate the computational complexity associated
with ML estimation, we adopt the Expectation Maximization (EM) algorithm. Due
to SNR diversity, the proposed multi-radio framework provides robustness to
channel SNR. Numerical results show the superiority of the proposed approach
with respect to single radio approaches as well as to modulation classifiers
using moments based estimators.
Markov random fields factorization with context-specific independences
Authors: Alejandro Edera, Facundo Bromberg, Federico Schlüter
Date: 2013-06-10 00:00:00

Markov random fields provide a compact representation of joint probability
distributions by representing its independence properties in an undirected
graph. The well-known Hammersley-Clifford theorem uses these conditional
independences to factorize a Gibbs distribution into a set of factors. However,
an important issue of using a graph to represent independences is that it
cannot encode some types of independence relations, such as the
context-specific independences (CSIs). They are a particular case of
conditional independences that is true only for a certain assignment of its
conditioning set; in contrast to conditional independences that must hold for
all its assignments. This work presents a method for factorizing a Markov
random field according to CSIs present in a distribution, and formally
guarantees that this factorization is correct. This is presented in our main
contribution, the context-specific Hammersley-Clifford theorem, a
generalization to CSIs of the Hammersley-Clifford theorem that applies for
conditional independences.
Asymptotically Optimal Sequential Estimation of the Mean Based on Inclusion Principle
Authors: Xinjia Chen
Date: 2013-06-10 00:00:00

A large class of problems in sciences and engineering can be formulated as
the general problem of constructing random intervals with pre-specified
coverage probabilities for the mean. Wee propose a general approach for
statistical inference of mean values based on accumulated observational data.
We show that the construction of such random intervals can be accomplished by
comparing the endpoints of random intervals with confidence sequences for the
mean. Asymptotic results are obtained for such sequential methods.
A Kernel Test for Three-Variable Interactions
Authors: Dino Sejdinovic, Arthur Gretton, Wicher Bergsma
Date: 2013-06-10 00:00:00
Proceeding: NeurIPS 2013 12

We introduce kernel nonparametric tests for Lancaster three-variable
interaction and for total independence, using embeddings of signed measures
into a reproducing kernel Hilbert space. The resulting test statistics are
straightforward to compute, and are used in powerful interaction tests, which
are consistent against all alternatives for a large family of reproducing
kernels. We show the Lancaster test to be sensitive to cases where two
independent causes individually have weak influence on a third dependent
variable, but their combined effect has a strong influence. This makes the
Lancaster test especially suited to finding structure in directed graphical
models, where it outperforms competing nonparametric tests in detecting such
V-structures.
Using the quaternion's representation of individuals in swarm intelligence and evolutionary computation
Authors: Iztok Fister, Iztok Fister Jr
Date: 2013-06-10 00:00:00
Tasks: Evolutionary Algorithms

This paper introduces a novel idea for representation of individuals using
quaternions in swarm intelligence and evolutionary algorithms. Quaternions are
a number system, which extends complex numbers. They are successfully applied
to problems of theoretical physics and to those areas needing fast rotation
calculations. We propose the application of quaternions in optimization, more
precisely, we have been using quaternions for representation of individuals in
Bat algorithm. The preliminary results of our experiments when optimizing a
test-suite consisting of ten standard functions showed that this new algorithm
significantly improved the results of the original Bat algorithm. Moreover, the
obtained results are comparable with other swarm intelligence and evolutionary
algorithms, like the artificial bees colony, and differential evolution. We
believe that this representation could also be successfully applied to other
swarm intelligence and evolutionary algorithms.
Detection of Outer Rotations on 3D-Vector Fields with Iterative Geometric Correlation and its Efficiency
Authors: Roxana Bujack, Gerik Scheuermann, Eckhard Hitzer
Date: 2013-06-10 00:00:00

Correlation is a common technique for the detection of shifts. Its
generalization to the multidimensional geometric correlation in Clifford
algebras has been proven a useful tool for color image processing, because it
additionally contains information about a rotational misalignment. But so far
the exact correction of a three-dimensional outer rotation could only be
achieved in certain special cases. In this paper we prove that applying the
geometric correlation iteratively has the potential to detect the outer
rotational misalignment for arbitrary three-dimensional vector fields. We
further present the explicit iterative algorithm, analyze its efficiency
detecting the rotational misalignment in the color space of a color image. The
experiments suggest a method for the acceleration of the algorithm, which is
practically tested with great success.
Combinatorial clustering and the beta negative binomial process
Authors: Tamara Broderick, Lester Mackey, John Paisley, Michael. I. Jordan
Date: 2011-11-08 00:00:00
Tasks: Clustering, Image Segmentation, Object Recognition, Semantic Segmentation

We develop a Bayesian nonparametric approach to a general family of latent
class problems in which individuals can belong simultaneously to multiple
classes and where each class can be exhibited multiple times by an individual.
We introduce a combinatorial stochastic process known as the negative binomial
process (NBP) as an infinite-dimensional prior appropriate for such problems.
We show that the NBP is conjugate to the beta process, and we characterize the
posterior distribution under the beta-negative binomial process (BNBP) and
hierarchical models based on the BNBP (the HBNBP). We study the asymptotic
properties of the BNBP and develop a three-parameter extension of the BNBP that
exhibits power-law behavior. We derive MCMC algorithms for posterior inference
under the HBNBP, and we present experiments using these algorithms in the
domains of image segmentation, object recognition, and document analysis.
Adaptive Noisy Clustering
Authors: Michael Chichignoud, Sébastien Loustau
Date: 2013-06-10 00:00:00
Tasks: Clustering

The problem of adaptive noisy clustering is investigated. Given a set of
noisy observations $Z_i=X_i+\epsilon_i$, $i=1,...,n$, the goal is to design
clusters associated with the law of $X_i$'s, with unknown density $f$ with
respect to the Lebesgue measure. Since we observe a corrupted sample, a direct
approach as the popular {\it $k$-means} is not suitable in this case. In this
paper, we propose a noisy $k$-means minimization, which is based on the
$k$-means loss function and a deconvolution estimator of the density $f$. In
particular, this approach suffers from the dependence on a bandwidth involved
in the deconvolution kernel. Fast rates of convergence for the excess risk are
proposed for a particular choice of the bandwidth, which depends on the
smoothness of the density $f$.
  Then, we turn out into the main issue of the paper: the data-driven choice of
the bandwidth. We state an adaptive upper bound for a new selection rule,
called ERC (Empirical Risk Comparison). This selection rule is based on the
Lepski's principle, where empirical risks associated with different bandwidths
are compared. Finally, we illustrate that this adaptive rule can be used in
many statistical problems of $M$-estimation where the empirical risk depends on
a nuisance parameter.
Image segmentation by optimal and hierarchical piecewise constant approximations
Authors: M. Kharinov
Date: 2013-06-10 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Piecewise constant image approximations of sequential number of segments or
clusters of disconnected pixels are treated. The method of majorizing of
optimal approximation sequence by hierarchical sequence of image approximations
is proposed. A generalization for multidimensional case of color and
multispectral images is foreseen.
"Not not bad" is not "bad": A distributional account of negation
Authors: Karl Moritz Hermann, Edward Grefenstette, Phil Blunsom
Date: 2013-06-10 00:00:00
Tasks: Negation

With the increasing empirical success of distributional models of
compositional semantics, it is timely to consider the types of textual logic
that such models are capable of capturing. In this paper, we address
shortcomings in the ability of current models to capture logical operations
such as negation. As a solution we propose a tripartite formulation for a
continuous vector space representation of semantics and subsequently use this
representation to develop a formal compositional notion of negation within such
models.
Dictionary Subselection Using an Overcomplete Joint Sparsity Model
Authors: Mehrdad Yaghoobi, Laurent Daudet, Michael E. Davies
Date: 2012-12-12 00:00:00
Tasks: Dictionary Learning, model

Many natural signals exhibit a sparse representation, whenever a suitable
describing model is given. Here, a linear generative model is considered, where
many sparsity-based signal processing techniques rely on such a simplified
model. As this model is often unknown for many classes of the signals, we need
to select such a model based on the domain knowledge or using some exemplar
signals. This paper presents a new exemplar based approach for the linear model
(called the dictionary) selection, for such sparse inverse problems. The
problem of dictionary selection, which has also been called the dictionary
learning in this setting, is first reformulated as a joint sparsity model. The
joint sparsity model here differs from the standard joint sparsity model as it
considers an overcompleteness in the representation of each signal, within the
range of selected subspaces. The new dictionary selection paradigm is examined
with some synthetic and realistic simulations.
Non-strongly-convex smooth stochastic approximation with convergence rate O(1/n)
Authors: Francis Bach, Eric Moulines
Date: 2013-06-10 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: BIG-bench Machine Learning, regression

We consider the stochastic approximation problem where a convex function has
to be minimized, given only the knowledge of unbiased estimates of its
gradients at certain points, a framework which includes machine learning
methods based on the minimization of the empirical risk. We focus on problems
without strong convexity, for which all previously known algorithms achieve a
convergence rate for function values of O(1/n^{1/2}). We consider and analyze
two algorithms that achieve a rate of O(1/n) for classical supervised learning
problems. For least-squares regression, we show that averaged stochastic
gradient descent with constant step-size achieves the desired rate. For
logistic regression, this is achieved by a simple novel stochastic gradient
algorithm that (a) constructs successive local quadratic approximations of the
loss functions, while (b) preserving the same running time complexity as
stochastic gradient descent. For these algorithms, we provide a non-asymptotic
analysis of the generalization error (in expectation, and also in high
probability for least-squares), and run extensive experiments on standard
machine learning benchmarks showing that they often outperform existing
approaches.
A Novel Approach for Single Gene Selection Using Clustering and Dimensionality Reduction
Authors: E. N. Sathishkumar, K. Thangavel, T. Chandrasekhar
Date: 2013-06-10 00:00:00
Tasks: Clustering, Dimensionality Reduction

We extend the standard rough set-based approach to deal with huge amounts of
numeric attributes versus small amount of available objects. Here, a novel
approach of clustering along with dimensionality reduction; Hybrid Fuzzy C
Means-Quick Reduct (FCMQR) algorithm is proposed for single gene selection.
Gene selection is a process to select genes which are more informative. It is
one of the important steps in knowledge discovery. The problem is that all
genes are not important in gene expression data. Some of the genes may be
redundant, and others may be irrelevant and noisy. In this study, the entire
dataset is divided in proper grouping of similar genes by applying Fuzzy C
Means (FCM) algorithm. A high class discriminated genes has been selected based
on their degree of dependence by applying Quick Reduct algorithm based on Rough
Set Theory to all the resultant clusters. Average Correlation Value (ACV) is
calculated for the high class discriminated genes. The clusters which have the
ACV value a s 1 is determined as significant clusters, whose classification
accuracy will be equal or high when comparing to the accuracy of the entire
dataset. The proposed algorithm is evaluated using WEKA classifiers and
compared. Finally, experimental results related to the leukemia cancer data
confirm that our approach is quite promising, though it surely requires further
research.
Discriminative k-means clustering
Authors: Ognjen Arandjelovic
Date: 2013-06-10 00:00:00
Tasks: Clustering, Face Recognition

The k-means algorithm is a partitional clustering method. Over 60 years old,
it has been successfully used for a variety of problems. The popularity of
k-means is in large part a consequence of its simplicity and efficiency. In
this paper we are inspired by these appealing properties of k-means in the
development of a clustering algorithm which accepts the notion of "positively"
and "negatively" labelled data. The goal is to discover the cluster structure
of both positive and negative data in a manner which allows for the
discrimination between the two sets. The usefulness of this idea is
demonstrated practically on the problem of face recognition, where the task of
learning the scope of a person's appearance should be done in a manner which
allows this face to be differentiated from others.
Discriminative extended canonical correlation analysis for pattern set matching
Authors: Ognjen Arandjelovic
Date: 2013-06-10 00:00:00
Tasks: Face Recognition, set matching

In this paper we address the problem of matching sets of vectors embedded in
the same input space. We propose an approach which is motivated by canonical
correlation analysis (CCA), a statistical technique which has proven successful
in a wide variety of pattern recognition problems. Like CCA when applied to the
matching of sets, our extended canonical correlation analysis (E-CCA) aims to
extract the most similar modes of variability within two sets. Our first major
contribution is the formulation of a principled framework for robust inference
of such modes from data in the presence of uncertainty associated with noise
and sampling randomness. E-CCA retains the efficiency and closed form
computability of CCA, but unlike it, does not possess free parameters which
cannot be inferred directly from data (inherent data dimensionality, and the
number of canonical correlations used for set similarity computation). Our
second major contribution is to show that in contrast to CCA, E-CCA is readily
adapted to match sets in a discriminative learning scheme which we call
discriminative extended canonical correlation analysis (DE-CCA). Theoretical
contributions of this paper are followed by an empirical evaluation of its
premises on the task of face recognition from sets of rasterized appearance
images. The results demonstrate that our approach, E-CCA, already outperforms
both CCA and its quasi-discriminative counterpart constrained CCA (C-CCA), for
all values of their free parameters. An even greater improvement is achieved
with the discriminative variant, DE-CCA.
Predicting Risk-of-Readmission for Congestive Heart Failure Patients: A Multi-Layer Approach
Authors: Kiyana Zolfaghar, Nele Verbiest, Jayshree Agarwal, Naren Meadem, Si-Chi Chin, Senjuti Basu Roy, Ankur Teredesai, David Hazel, Paul Amoroso, Lester Reed
Date: 2013-06-10 00:00:00
Tasks: General Classification

Mitigating risk-of-readmission of Congestive Heart Failure (CHF) patients
within 30 days of discharge is important because such readmissions are not only
expensive but also critical indicator of provider care and quality of
treatment. Accurately predicting the risk-of-readmission may allow hospitals to
identify high-risk patients and eventually improve quality of care by
identifying factors that contribute to such readmissions in many scenarios. In
this paper, we investigate the problem of predicting risk-of-readmission as a
supervised learning problem, using a multi-layer classification approach.
Earlier contributions inadequately attempted to assess a risk value for 30 day
readmission by building a direct predictive model as opposed to our approach.
We first split the problem into various stages, (a) at risk in general (b) risk
within 60 days (c) risk within 30 days, and then build suitable classifiers for
each stage, thereby increasing the ability to accurately predict the risk using
multiple layers of decision. The advantage of our approach is that we can use
different classification models for the subtasks that are more suited for the
respective problems. Moreover, each of the subtasks can be solved using
different features and training data leading to a highly confident diagnosis or
risk compared to a one-shot single layer approach. An experimental evaluation
on actual hospital patient record data from Multicare Health Systems shows that
our model is significantly better at predicting risk-of-readmission of CHF
patients within 30 days after discharge compared to prior attempts.
Logistic Tensor Factorization for Multi-Relational Data
Authors: Maximilian Nickel, Volker Tresp
Date: 2013-06-10 00:00:00
Tasks: Relational Reasoning

Tensor factorizations have become increasingly popular approaches for various
learning tasks on structured data. In this work, we extend the RESCAL tensor
factorization, which has shown state-of-the-art results for multi-relational
learning, to account for the binary nature of adjacency tensors. We study the
improvements that can be gained via this approach on various benchmark datasets
and show that the logistic extension can improve the prediction results
significantly.
3D model retrieval using global and local radial distances
Authors: Bo Li, Henry Johan
Date: 2013-06-10 00:00:00
Tasks: Retrieval

3D model retrieval techniques can be classified as histogram-based,
view-based and graph-based approaches. We propose a hybrid shape descriptor
which combines the global and local radial distance features by utilizing the
histogram-based and view-based approaches respectively. We define an
area-weighted global radial distance with respect to the center of the bounding
sphere of the model and encode its distribution into a 2D histogram as the
global radial distance shape descriptor. We then uniformly divide the bounding
cube of a 3D model into a set of small cubes and define their centers as local
centers. Then, we compute the local radial distance of a point based on the
nearest local center. By sparsely sampling a set of views and encoding the
local radial distance feature on the rendered views by color coding, we extract
the local radial distance shape descriptor. Based on these two shape
descriptors, we develop a hybrid radial distance shape descriptor for 3D model
retrieval. Experiment results show that our hybrid shape descriptor outperforms
several typical histogram-based and view-based approaches.
Minimax Theory for High-dimensional Gaussian Mixtures with Sparse Mean Separation
Authors: Martin Azizyan, Aarti Singh, Larry Wasserman
Date: 2013-06-09 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Clustering, feature selection, Vocal Bursts Intensity Prediction

While several papers have investigated computationally and statistically
efficient methods for learning Gaussian mixtures, precise minimax bounds for
their statistical performance as well as fundamental limits in high-dimensional
settings are not well-understood. In this paper, we provide precise information
theoretic bounds on the clustering accuracy and sample complexity of learning a
mixture of two isotropic Gaussians in high dimensions under small mean
separation. If there is a sparse subset of relevant dimensions that determine
the mean separation, then the sample complexity only depends on the number of
relevant dimensions and mean separation, and can be achieved by a simple
computationally efficient procedure. Our results provide the first step of a
theoretical basis for recent methods that combine feature selection and
clustering.