pwc-archive/papers-with-abstracts huggingface dataset:
Extreme Entropy Machines: Robust information theoretic classification
Authors: Wojciech Marian Czarnecki, Jacek Tabor
Date: 2015-01-21 00:00:00
Tasks: Classification, General Classification

Most of the existing classification methods are aimed at minimization of
empirical risk (through some simple point-based error measured with loss
function) with added regularization. We propose to approach this problem in a
more information theoretic way by investigating applicability of entropy
measures as a classification model objective function. We focus on quadratic
Renyi's entropy and connected Cauchy-Schwarz Divergence which leads to the
construction of Extreme Entropy Machines (EEM).
  The main contribution of this paper is proposing a model based on the
information theoretic concepts which on the one hand shows new, entropic
perspective on known linear classifiers and on the other leads to a
construction of very robust method competetitive with the state of the art
non-information theoretic ones (including Support Vector Machines and Extreme
Learning Machines).
  Evaluation on numerous problems spanning from small, simple ones from UCI
repository to the large (hundreads of thousands of samples) extremely
unbalanced (up to 100:1 classes' ratios) datasets shows wide applicability of
the EEM in real life problems and that it scales well.
Trolls Identification within an Uncertain Framework
Authors: Imen Ouled Dlala, Dorra Attiaoui, Arnaud Martin, Boutheina Ben Yaghlane
Date: 2015-01-21 00:00:00

The web plays an important role in people's social lives since the emergence
of Web 2.0. It facilitates the interaction between users, gives them the
possibility to freely interact, share and collaborate through social networks,
online communities forums, blogs, wikis and other online collaborative media.
However, an other side of the web is negatively taken such as posting
inflammatory messages. Thus, when dealing with the online communities forums,
the managers seek to always enhance the performance of such platforms. In fact,
to keep the serenity and prohibit the disturbance of the normal atmosphere,
managers always try to novice users against these malicious persons by posting
such message (DO NOT FEED TROLLS). But, this kind of warning is not enough to
reduce this phenomenon. In this context we propose a new approach for detecting
malicious people also called 'Trolls' in order to allow community managers to
take their ability to post online. To be more realistic, our proposal is
defined within an uncertain framework. Based on the assumption consisting on
the trolls' integration in the successful discussion threads, we try to detect
the presence of such malicious users. Indeed, this method is based on a
conflict measure of the belief function theory applied between the different
messages of the thread. In order to show the feasibility and the result of our
approach, we test it in different simulated data.
Plug-and-play dual-tree algorithm runtime analysis
Authors: Ryan R. Curtin, Dongryeol Lee, William B. March, Parikshit Ram
Date: 2015-01-21 00:00:00
Tasks: Density Estimation

Numerous machine learning algorithms contain pairwise statistical problems at
their core---that is, tasks that require computations over all pairs of input
points if implemented naively. Often, tree structures are used to solve these
problems efficiently. Dual-tree algorithms can efficiently solve or approximate
many of these problems. Using cover trees, rigorous worst-case runtime
guarantees have been proven for some of these algorithms. In this paper, we
present a problem-independent runtime guarantee for any dual-tree algorithm
using the cover tree, separating out the problem-dependent and the
problem-independent elements. This allows us to just plug in bounds for the
problem-dependent elements to get runtime guarantees for dual-tree algorithms
for any pairwise statistical problem without re-deriving the entire proof. We
demonstrate this plug-and-play procedure for nearest-neighbor search and
approximate kernel density estimation to get improved runtime guarantees. Under
mild assumptions, we also present the first linear runtime guarantee for
dual-tree based range search.
A Separation Theorem for Chain Event Graphs
Authors: Peter A. Thwaites, Jim Q. Smith
Date: 2015-01-21 00:00:00

Bayesian Networks (BNs) are popular graphical models for the representation
of statistical problems embodying dependence relationships between a number of
variables. Much of this popularity is due to the d-separation theorem of Pearl
and Lauritzen, which allows an analyst to identify the conditional independence
statements that a model of the problem embodies using only the topology of the
graph. However for many problems the complete model dependence structure cannot
be depicted by a BN. The Chain Event Graph (CEG) was introduced for these types
of problem. In this paper we introduce a separation theorem for CEGs, analogous
to the d-separation theorem for BNs, which likewise allows an analyst to
identify the conditional independence structure of their model from the
topology of the graph.
Minimax Optimal Sparse Signal Recovery with Poisson Statistics
Authors: Mohammad H. Rohban, Delaram Motamedvaziri, Venkatesh Saligrama
Date: 2015-01-21 00:00:00
Tasks: Decoder, Marketing

We are motivated by problems that arise in a number of applications such as
Online Marketing and Explosives detection, where the observations are usually
modeled using Poisson statistics. We model each observation as a Poisson random
variable whose mean is a sparse linear superposition of known patterns. Unlike
many conventional problems observations here are not identically distributed
since they are associated with different sensing modalities. We analyze the
performance of a Maximum Likelihood (ML) decoder, which for our Poisson setting
involves a non-linear optimization but yet is computationally tractable. We
derive fundamental sample complexity bounds for sparse recovery when the
measurements are contaminated with Poisson noise. In contrast to the
least-squares linear regression setting with Gaussian noise, we observe that in
addition to sparsity, the scale of the parameters also fundamentally impacts
$\ell_2$ error in the Poisson setting. We show tightness of our upper bounds
both theoretically and experimentally. In particular, we derive a minimax
matching lower bound on the mean-squared error and show that our constrained ML
decoder is minimax optimal for this regime.
Mirror, mirror on the wall, tell me, is the error small?
Authors: Heng Yang, Ioannis Patras
Date: 2015-01-21 00:00:00
Proceeding: CVPR 2015 6
Tasks: Face Alignment, Pose Estimation

Do object part localization methods produce bilaterally symmetric results on
mirror images? Surprisingly not, even though state of the art methods augment
the training set with mirrored images. In this paper we take a closer look into
this issue. We first introduce the concept of mirrorability as the ability of a
model to produce symmetric results in mirrored images and introduce a
corresponding measure, namely the \textit{mirror error} that is defined as the
difference between the detection result on an image and the mirror of the
detection result on its mirror image. We evaluate the mirrorability of several
state of the art algorithms in two of the most intensively studied problems,
namely human pose estimation and face alignment. Our experiments lead to
several interesting findings: 1) Surprisingly, most of state of the art methods
struggle to preserve the mirror symmetry, despite the fact that they do have
very similar overall performance on the original and mirror images; 2) the low
mirrorability is not caused by training or testing sample bias - all algorithms
are trained on both the original images and their mirrored versions; 3) the
mirror error is strongly correlated to the localization/alignment error (with
correlation coefficients around 0.7). Since the mirror error is calculated
without knowledge of the ground truth, we show two interesting applications -
in the first it is used to guide the selection of difficult samples and in the
second to give feedback in a popular Cascaded Pose Regression method for face
alignment.
Lazier ABC
Authors: Dennis Prangle
Date: 2015-01-21 00:00:00

ABC algorithms involve a large number of simulations from the model of
interest, which can be very computationally costly. This paper summarises the
lazy ABC algorithm of Prangle (2015), which reduces the computational demand by
abandoning many unpromising simulations before completion. By using a random
stopping decision and reweighting the output sample appropriately, the target
distribution is the same as for standard ABC. Lazy ABC is also extended here to
the case of non-uniform ABC kernels, which is shown to simplify the process of
tuning the algorithm effectively.
A General Theory of Hypothesis Tests and Confidence Regions for Sparse High Dimensional Models
Authors: Yang Ning, Han Liu
Date: 2014-12-30 00:00:00

We consider the problem of uncertainty assessment for low dimensional
components in high dimensional models. Specifically, we propose a decorrelated
score function to handle the impact of high dimensional nuisance parameters. We
consider both hypothesis tests and confidence regions for generic penalized
M-estimators. Unlike most existing inferential methods which are tailored for
individual models, our approach provides a general framework for high
dimensional inference and is applicable to a wide range of applications. From
the testing perspective, we develop general theorems to characterize the
limiting distributions of the decorrelated score test statistic under both null
hypothesis and local alternatives. These results provide asymptotic guarantees
on the type I errors and local powers of the proposed test. Furthermore, we
show that the decorrelated score function can be used to construct point and
confidence region estimators that are semiparametrically efficient. We also
generalize this framework to broaden its applications. First, we extend it to
handle high dimensional null hypothesis, where the number of parameters of
interest can increase exponentially fast with the sample size. Second, we
establish the theory for model misspecification. Third, we go beyond the
likelihood framework, by introducing the generalized score test based on
general loss functions. Thorough numerical studies are conducted to back up the
developed theoretical results.
Tracking an Object with Unknown Accelerations using a Shadowing Filter
Authors: Kevin Judd
Date: 2015-01-21 00:00:00
Tasks: State Estimation

A commonly encountered problem is the tracking of a physical object, like a
maneuvering ship, aircraft, land vehicle, spacecraft or animate creature
carrying a wireless device. The sensor data is often limited and inaccurate
observations of range or bearing. This problem is more difficult than tracking
a ballistic trajectory, because an operative affects unknown and arbitrarily
changing accelerations. Although stochastic methods of filtering or state
estimation (Kalman filters and particle filters) are widely used, out of vogue
variational methods are more appropriate in this tracking context, because the
objects do not typically display any significant random motions at the length
and time scales of interest. This leads us to propose a rather elegant approach
based on a \emph{shadowing filter}. The resulting filter is efficient (reduces
to the solution of linear equations) and robust (uneffected by missing data and
singular correlations that would cause catastrophic failure of Bayesian
filters.) The tracking is so robust, that in some common situations it actually
performs better by ignoring error correlations that are so vital to Kalman
filters.
Convergent Bayesian formulations of blind source separation and electromagnetic source estimation
Authors: Kevin H. Knuth, Herbert G. Vaughan Jr
Date: 2015-01-21 00:00:00
Tasks: blind source separation

We consider two areas of research that have been developing in parallel over
the last decade: blind source separation (BSS) and electromagnetic source
estimation (ESE). BSS deals with the recovery of source signals when only
mixtures of signals can be obtained from an array of detectors and the only
prior knowledge consists of some information about the nature of the source
signals. On the other hand, ESE utilizes knowledge of the electromagnetic
forward problem to assign source signals to their respective generators, while
information about the signals themselves is typically ignored. We demonstrate
that these two techniques can be derived from the same starting point using the
Bayesian formalism. This suggests a means by which new algorithms can be
developed that utilize as much relevant information as possible. We also
briefly mention some preliminary work that supports the value of integrating
information used by these two techniques and review the kinds of information
that may be useful in addressing the ESE problem.
Difficulties applying recent blind source separation techniques to EEG and MEG
Authors: Kevin H. Knuth
Date: 2015-01-21 00:00:00
Tasks: blind source separation, EEG, Electroencephalogram (EEG)

High temporal resolution measurements of human brain activity can be
performed by recording the electric potentials on the scalp surface
(electroencephalography, EEG), or by recording the magnetic fields near the
surface of the head (magnetoencephalography, MEG). The analysis of the data is
problematic due to the fact that multiple neural generators may be
simultaneously active and the potentials and magnetic fields from these sources
are superimposed on the detectors. It is highly desirable to un-mix the data
into signals representing the behaviors of the original individual generators.
This general problem is called blind source separation and several recent
techniques utilizing maximum entropy, minimum mutual information, and maximum
likelihood estimation have been applied. These techniques have had much success
in separating signals such as natural sounds or speech, but appear to be
ineffective when applied to EEG or MEG signals. Many of these techniques
implicitly assume that the source distributions have a large kurtosis, whereas
an analysis of EEG/MEG signals reveals that the distributions are multimodal.
This suggests that more effective separation techniques could be designed for
EEG and MEG signals.
Abrupt Motion Tracking via Nearest Neighbor Field Driven Stochastic Sampling
Authors: Tianfei Zhou, Yao Lu, Feng Lv, Huijun Di, Qingjie Zhao, Jian Zhang
Date: 2014-10-28 00:00:00
Tasks: Motion Detection

Stochastic sampling based trackers have shown good performance for abrupt
motion tracking so that they have gained popularity in recent years. However,
conventional methods tend to use a two-stage sampling paradigm, in which the
search space needs to be uniformly explored with an inefficient preliminary
sampling phase. In this paper, we propose a novel sampling-based method in the
Bayesian filtering framework to address the problem. Within the framework,
nearest neighbor field estimation is utilized to compute the importance
proposal probabilities, which guide the Markov chain search towards promising
regions and thus enhance the sampling efficiency; given the motion priors, a
smoothing stochastic sampling Monte Carlo algorithm is proposed to approximate
the posterior distribution through a smoothing weight-updating scheme.
Moreover, to track the abrupt and the smooth motions simultaneously, we develop
an abrupt-motion detection scheme which can discover the presence of abrupt
motions during online tracking. Extensive experiments on challenging image
sequences demonstrate the effectiveness and the robustness of our algorithm in
handling the abrupt motions.
Maximin Safety: When Failing to Lose is Preferable to Trying to Win
Authors: Brad Gulko, Samantha Leung
Date: 2015-01-21 00:00:00

We present a new decision rule, \emph{maximin safety}, that seeks to maintain
a large margin from the worst outcome, in much the same way minimax regret
seeks to minimize distance from the best. We argue that maximin safety is
valuable both descriptively and normatively. Descriptively, maximin safety
explains the well-known \emph{decoy effect}, in which the introduction of a
dominated option changes preferences among the other options. Normatively, we
provide an axiomatization that characterizes preferences induced by maximin
safety, and show that maximin safety shares much of the same behavioral basis
with minimax regret.
Regroupement sémantique de définitions en espagnol
Authors: Gerardo Sierra, Juan-Manuel Torres-Moreno, Alejandro Molina
Date: 2015-01-20 00:00:00
Tasks: Clustering

This article focuses on the description and evaluation of a new unsupervised
learning method of clustering of definitions in Spanish according to their
semantic. Textual Energy was used as a clustering measure, and we study an
adaptation of the Precision and Recall to evaluate our method.
Scalable Multi-Output Label Prediction: From Classifier Chains to Classifier Trellises
Authors: J. Read, L. Martino, P. Olmos, D. Luengo
Date: 2015-01-20 00:00:00
Tasks: Classification, General Classification, Multi-Label Classification, MUlTI-LABEL-ClASSIFICATION

Multi-output inference tasks, such as multi-label classification, have become
increasingly important in recent years. A popular method for multi-label
classification is classifier chains, in which the predictions of individual
classifiers are cascaded along a chain, thus taking into account inter-label
dependencies and improving the overall performance. Several varieties of
classifier chain methods have been introduced, and many of them perform very
competitively across a wide range of benchmark datasets. However, scalability
limitations become apparent on larger datasets when modeling a fully-cascaded
chain. In particular, the methods' strategies for discovering and modeling a
good chain structure constitutes a mayor computational bottleneck. In this
paper, we present the classifier trellis (CT) method for scalable multi-label
classification. We compare CT with several recently proposed classifier chain
methods to show that it occupies an important niche: it is highly competitive
on standard multi-label problems, yet it can also scale up to thousands or even
tens of thousands of labels.
Big Data: How Geo-information Helped Shape the Future of Data Engineering
Authors: Robert Jeansoulin
Date: 2015-01-20 00:00:00

Very large data sets are the common rule in automated mapping, GIS, remote
sensing, and what we can name geo-information. Indeed, in 1983 Landsat was
already delivering gigabytes of data, and other sensors were in orbit or ready
for launch, and a tantamount of cartographic data was being digitized. The
retrospective paper revisits several issues that geo-information sciences had
to face from the early stages on, including: structure ( to bring some
structure to the data registered from a sampled signal, metadata); processing
(huge amounts of data for big computers and fast algorithms); uncertainty (the
kinds of errors, their quantification); consistency (when merging different
sources of data is logically allowed, and meaningful); ontologies (clear and
agreed shared definitions, if any kind of decision should be based upon them).
All these issues are the background of Internet queries, and the underlying
technology has been shaped during those years when geo-information engineering
emerged.
Separation of undersampled composite signals using the Dantzig selector with overcomplete dictionaries
Authors: Ashley Prater, Lixin Shen
Date: 2015-01-20 00:00:00
Tasks: Compressive Sensing

In many applications one may acquire a composition of several signals that
may be corrupted by noise, and it is a challenging problem to reliably separate
the components from one another without sacrificing significant details. Adding
to the challenge, in a compressive sensing framework, one is given only an
undersampled set of linear projections of the composite signal. In this paper,
we propose using the Dantzig selector model incorporating an overcomplete
dictionary to separate a noisy undersampled collection of composite signals,
and present an algorithm to efficiently solve the model.
  The Dantzig selector is a statistical approach to finding a solution to a
noisy linear regression problem by minimizing the $\ell_1$ norm of candidate
coefficient vectors while constraining the scope of the residuals. If the
underlying coefficient vector is sparse, then the Dantzig selector performs
well in the recovery and separation of the unknown composite signal. In the
following, we propose a proximity operator based algorithm to recover and
separate unknown noisy undersampled composite signals through the Dantzig
selector. We present numerical simulations comparing the proposed algorithm
with the competing Alternating Direction Method, and the proposed algorithm is
found to be faster, while producing similar quality results. Additionally, we
demonstrate the utility of the proposed algorithm in several experiments by
applying it in various domain applications including the recovery of
complex-valued coefficient vectors, the removal of impulse noise from smooth
signals, and the separation and classification of a composition of handwritten
digits.
What do we learn about development from baby robots?
Authors: Pierre-Yves Oudeyer
Date: 2015-01-20 00:00:00

Understanding infant development is one of the greatest scientific challenges
of contemporary science. A large source of difficulty comes from the fact that
the development of skills in infants results from the interactions of multiple
mechanisms at multiple spatio-temporal scales. The concepts of "innate" or
"acquired" are not any more adequate tools for explanations, which call for a
shift from reductionist to systemic accounts. To address this challenge,
building and experimenting with robots modeling the growing infant brain and
body is crucial. Systemic explanations of pattern formation in sensorimotor,
cognitive and social development, viewed as a complex dynamical system, require
the use of formal models based on mathematics, algorithms and robots.
Formulating hypothesis about development using such models, and exploring them
through experiments, allows us to consider in detail the interaction between
many mechanisms and parameters. This complements traditional experimental
methods in psychology and neuroscience where only a few variables can be
studied at the same time. Furthermore, the use of robots is of particular
importance. The laws of physics generate everywhere around us spontaneous
patterns in the inorganic world. They also strongly impact the living, and in
particular constrain and guide infant development through the properties of its
(changing) body in interaction with the physical environment. Being able to
consider the body as an experimental variable, something that can be
systematically changed in order to study the impact on skill formation, has
been a dream to many developmental scientists. This is today becoming possible
with developmental robotics.
Belief Approach for Social Networks
Authors: Salma Ben Dhaou, Mouloud Kharoune, Arnaud Martin, Boutheina Ben Yaghlane
Date: 2015-01-20 00:00:00

Nowadays, social networks became essential in information exchange between
individuals. Indeed, as users of these networks, we can send messages to other
people according to the links connecting us. Moreover, given the large volume
of exchanged messages, detecting the true nature of the received message
becomes a challenge. For this purpose, it is interesting to consider this new
tendency with reasoning under uncertainty by using the theory of belief
functions. In this paper, we tried to model a social network as being a network
of fusion of information and determine the true nature of the received message
in a well-defined node by proposing a new model: the belief social network.