pwc-archive/papers-with-abstracts huggingface dataset:
Smoothed Functional Algorithms for Stochastic Optimization using q-Gaussian Distributions
Authors: Debarghya Ghoshdastidar, Ambedkar Dukkipati, Shalabh Bhatnagar
Date: 2012-06-21 00:00:00
Tasks: Stochastic Optimization

Smoothed functional (SF) schemes for gradient estimation are known to be
efficient in stochastic optimization algorithms, specially when the objective
is to improve the performance of a stochastic system. However, the performance
of these methods depends on several parameters, such as the choice of a
suitable smoothing kernel. Different kernels have been studied in literature,
which include Gaussian, Cauchy and uniform distributions among others. This
paper studies a new class of kernels based on the q-Gaussian distribution, that
has gained popularity in statistical physics over the last decade. Though the
importance of this family of distributions is attributed to its ability to
generalize the Gaussian distribution, we observe that this class encompasses
almost all existing smoothing kernels. This motivates us to study SF schemes
for gradient estimation using the q-Gaussian distribution. Using the derived
gradient estimates, we propose two-timescale algorithms for optimization of a
stochastic objective function in a constrained setting with projected gradient
search approach. We prove the convergence of our algorithms to the set of
stationary points of an associated ODE. We also demonstrate their performance
numerically through simulations on a queuing model.
BiofilmQuant: A Computer-Assisted Tool for Dental Biofilm Quantification
Authors: Awais Mansoor, Valery Patsekin, Dale Scherl, J. Paul Robinson, Bartlomiej Rajwa
Date: 2014-07-03 00:00:00

Dental biofilm is the deposition of microbial material over a tooth
substratum. Several methods have recently been reported in the literature for
biofilm quantification; however, at best they provide a barely automated
solution requiring significant input needed from the human expert. On the
contrary, state-of-the-art automatic biofilm methods fail to make their way
into clinical practice because of the lack of effective mechanism to
incorporate human input to handle praxis or misclassified regions. Manual
delineation, the current gold standard, is time consuming and subject to expert
bias. In this paper, we introduce a new semi-automated software tool,
BiofilmQuant, for dental biofilm quantification in quantitative light-induced
fluorescence (QLF) images. The software uses a robust statistical modeling
approach to automatically segment the QLF image into three classes (background,
biofilm, and tooth substratum) based on the training data. This initial
segmentation has shown a high degree of consistency and precision on more than
200 test QLF dental scans. Further, the proposed software provides the
clinicians full control to fix any misclassified areas using a single click. In
addition, BiofilmQuant also provides a complete solution for the longitudinal
quantitative analysis of biofilm of the full set of teeth, providing greater
ease of usability.
Structured Learning via Logistic Regression
Authors: Justin Domke
Date: 2014-07-03 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: regression

A successful approach to structured learning is to write the learning
objective as a joint function of linear parameters and inference messages, and
iterate between updates to each. This paper observes that if the inference
problem is "smoothed" through the addition of entropy terms, for fixed
messages, the learning objective reduces to a traditional (non-structured)
logistic regression problem with respect to parameters. In these logistic
regression problems, each training example has a bias term determined by the
current set of messages. Based on this insight, the structured energy function
can be extended from linear factors to any function class where an "oracle"
exists to minimize a logistic loss.
Deep Poselets for Human Detection
Authors: Lubomir Bourdev, Fei Yang, Rob Fergus
Date: 2014-07-02 00:00:00
Tasks: Human Detection

We address the problem of detecting people in natural scenes using a part
approach based on poselets. We propose a bootstrapping method that allows us to
collect millions of weakly labeled examples for each poselet type. We use these
examples to train a Convolutional Neural Net to discriminate different poselet
types and separate them from the background class. We then use the trained CNN
as a way to represent poselet patches with a Pose Discriminative Feature (PDF)
vector -- a compact 256-dimensional feature vector that is effective at
discriminating pose from appearance. We train the poselet model on top of PDF
features and combine them with object-level CNNs for detection and bounding box
prediction. The resulting model leads to state-of-the-art performance for human
detection on the PASCAL datasets.
Continuous On-line Evolution of Agent Behaviours with Cartesian Genetic Programming
Authors: Davide Nunes, Luis Antunes
Date: 2014-07-02 00:00:00
Tasks: Evolutionary Algorithms

Evolutionary Computation has been successfully used to synthesise controllers
for embodied agents and multi-agent systems in general. Notwithstanding this,
continuous on-line adaptation by the means of evolutionary algorithms is still
under-explored, especially outside the evolutionary robotics domain. In this
paper, we present an on-line evolutionary programming algorithm that searches
in the agent design space for the appropriate behavioural policies to cope with
the underlying environment. We discuss the current problems of continuous agent
adaptation, present our on-line evolution testbed for evolutionary simulation.
Nonparametric Hierarchical Clustering of Functional Data
Authors: Marc Boullé, Romain Guigourès, Fabrice Rossi
Date: 2014-07-02 00:00:00
Tasks: Clustering, Model Selection

In this paper, we deal with the problem of curves clustering. We propose a
nonparametric method which partitions the curves into clusters and discretizes
the dimensions of the curve points into intervals. The cross-product of these
partitions forms a data-grid which is obtained using a Bayesian model selection
approach while making no assumptions regarding the curves. Finally, a
post-processing technique, aiming at reducing the number of clusters in order
to improve the interpretability of the clustering, is proposed. It consists in
optimally merging the clusters step by step, which corresponds to an
agglomerative hierarchical classification whose dissimilarity measure is the
variation of the criterion. Interestingly this measure is none other than the
sum of the Kullback-Leibler divergences between clusters distributions before
and after the merges. The practical interest of the approach for functional
data exploratory analysis is presented and compared with an alternative
approach on an artificial and a real world data set.
How Many Dissimilarity/Kernel Self Organizing Map Variants Do We Need?
Authors: Fabrice Rossi
Date: 2014-07-02 00:00:00

In numerous applicative contexts, data are too rich and too complex to be
represented by numerical vectors. A general approach to extend machine learning
and data mining techniques to such data is to really on a dissimilarity or on a
kernel that measures how different or similar two objects are. This approach
has been used to define several variants of the Self Organizing Map (SOM). This
paper reviews those variants in using a common set of notations in order to
outline differences and similarities between them. It discusses the advantages
and drawbacks of the variants, as well as the actual relevance of the
dissimilarity/kernel SOM for practical applications.
Higher-Order Quantum-Inspired Genetic Algorithms
Authors: Robert Nowotniak, Jacek Kucharski
Date: 2014-07-02 00:00:00
Tasks: Combinatorial Optimization

This paper presents a theory and an empirical evaluation of Higher-Order
Quantum-Inspired Genetic Algorithms. Fundamental notions of the theory have
been introduced, and a novel Order-2 Quantum-Inspired Genetic Algorithm (QIGA2)
has been presented. Contrary to all QIGA algorithms which represent quantum
genes as independent qubits, in higher-order QIGAs quantum registers are used
to represent genes strings which allows modelling of genes relations using
quantum phenomena. Performance comparison has been conducted on a benchmark of
20 deceptive combinatorial optimization problems. It has been presented that
using higher quantum orders is beneficial for genetic algorithm efficiency, and
the new QIGA2 algorithm outperforms the old QIGA algorithm which was tuned in
highly compute intensive metaoptimization process.
On the Consistency of AUC Pairwise Optimization
Authors: Wei Gao, Zhi-Hua Zhou
Date: 2012-08-03 00:00:00
Tasks: Learning-To-Rank

AUC (area under ROC curve) is an important evaluation criterion, which has
been popularly used in many learning tasks such as class-imbalance learning,
cost-sensitive learning, learning to rank, etc. Many learning approaches try to
optimize AUC, while owing to the non-convexity and discontinuousness of AUC,
almost all approaches work with surrogate loss functions. Thus, the consistency
of AUC is crucial; however, it has been almost untouched before. In this paper,
we provide a sufficient condition for the asymptotic consistency of learning
approaches based on surrogate loss functions. Based on this result, we prove
that exponential loss and logistic loss are consistent with AUC, but hinge loss
is inconsistent. Then, we derive the $q$-norm hinge loss and general hinge loss
that are consistent with AUC. We also derive the consistent bounds for
exponential loss and logistic loss, and obtain the consistent bounds for many
surrogate loss functions under the non-noise setting. Further, we disclose an
equivalence between the exponential surrogate loss of AUC and exponential
surrogate loss of accuracy, and one straightforward consequence of such finding
is that AdaBoost and RankBoost are equivalent.
Dropout Rademacher Complexity of Deep Neural Networks
Authors: Wei Gao, Zhi-Hua Zhou
Date: 2014-02-16 00:00:00

Great successes of deep neural networks have been witnessed in various real
applications. Many algorithmic and implementation techniques have been
developed, however, theoretical understanding of many aspects of deep neural
networks is far from clear. A particular interesting issue is the usefulness of
dropout, which was motivated from the intuition of preventing complex
co-adaptation of feature detectors. In this paper, we study the Rademacher
complexity of different types of dropout, and our theoretical results disclose
that for shallow neural networks (with one or none hidden layer) dropout is
able to reduce the Rademacher complexity in polynomial, whereas for deep neural
networks it can amazingly lead to an exponential reduction of the Rademacher
complexity.
Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics
Authors: Jorge Gomes, Pedro Mariano, Anders Lyhne Christensen
Date: 2014-07-02 00:00:00
Tasks: Diversity

Evolutionary techniques driven by behavioural diversity, such as novelty
search, have shown significant potential in evolutionary robotics. These
techniques rely on priorly specified behaviour characterisations to estimate
the similarity between individuals. Characterisations are typically defined in
an ad hoc manner based on the experimenter's intuition and knowledge about the
task. Alternatively, generic characterisations based on the sensor-effector
values of the agents are used. In this paper, we propose a novel approach that
allows for systematic derivation of behaviour characterisations for
evolutionary robotics, based on a formal description of the agents and their
environment. Systematically derived behaviour characterisations (SDBCs) go
beyond generic characterisations in that they can contain task-specific
features related to the internal state of the agents, environmental features,
and relations between them. We evaluate SDBCs with novelty search in three
simulated collective robotics tasks. Our results show that SDBCs yield a
performance comparable to the task-specific characterisations, in terms of both
solution quality and behaviour space exploration.
Novelty Search in Competitive Coevolution
Authors: Jorge Gomes, Pedro Mariano, Anders Lyhne Christensen
Date: 2014-07-02 00:00:00

One of the main motivations for the use of competitive coevolution systems is
their ability to capitalise on arms races between competing species to evolve
increasingly sophisticated solutions. Such arms races can, however, be hard to
sustain, and it has been shown that the competing species often converge
prematurely to certain classes of behaviours. In this paper, we investigate if
and how novelty search, an evolutionary technique driven by behavioural
novelty, can overcome convergence in coevolution. We propose three methods for
applying novelty search to coevolutionary systems with two species: (i) score
both populations according to behavioural novelty; (ii) score one population
according to novelty, and the other according to fitness; and (iii) score both
populations with a combination of novelty and fitness. We evaluate the methods
in a predator-prey pursuit task. Our results show that novelty-based approaches
can evolve a significantly more diverse set of solutions, when compared to
traditional fitness-based coevolution.
A Theoretical and Experimental Comparison of the EM and SEM Algorithm
Authors: Johannes Blömer, Kathrin Bujna, Daniel Kuntze
Date: 2013-10-18 00:00:00

In this paper we provide a new analysis of the SEM algorithm. Unlike previous
work, we focus on the analysis of a single run of the algorithm. First, we
discuss the algorithm for general mixture distributions. Second, we consider
Gaussian mixture models and show that with high probability the update
equations of the EM algorithm and its stochastic variant are almost the same,
given that the input set is sufficiently large. Our experiments confirm that
this still holds for a large number of successive update steps. In particular,
for Gaussian mixture models, we show that the stochastic variant runs nearly
twice as fast.
Semantic Integration & Single-Site Opening of Multiple Governmental Data Sources
Authors: Konstantinos Kotis, Iraklis Athanasakis, George Vouros
Date: 2014-07-02 00:00:00

In many cases, government data is still "locked" in several "data silos",
even within the boundaries of a single (inter-)national public organization
with disparate and distributed organizational units and departments spread
across multiple sites. Opening data and enabling its unified querying from a
single site in an efficient and effective way is a semantic application
integration and open government data challenge. This paper describes how NARA
is using Semantic Web technology to implement an application integration
approach within the boundaries of its organization via opening and querying
multiple governmental data sources from a single site. The generic approach
proposed, namely S3-AI, provides support to answering unified,
ontology-mediated, federated queries to data produced and exploited by
disparate applications, while these are being located in different
organizational sites. S3-AI preserves ownership, autonomy and independency of
applications and data. The paper extensively demonstrates S3-AI, using the D2RQ
and Fuseki technologies, for addressing the needs of a governmental "IT
helpdesk support" case.
DC approximation approaches for sparse optimization
Authors: Hoai An Le Thi, Tao Pham Dinh, Hoai Minh Le, Xuan Thanh Vo
Date: 2014-07-01 00:00:00
Tasks: feature selection

Sparse optimization refers to an optimization problem involving the zero-norm
in objective or constraints. In this paper, nonconvex approximation approaches
for sparse optimization have been studied with a unifying point of view in DC
(Difference of Convex functions) programming framework. Considering a common DC
approximation of the zero-norm including all standard sparse inducing penalty
functions, we studied the consistency between global minimums (resp. local
minimums) of approximate and original problems. We showed that, in several
cases, some global minimizers (resp. local minimizers) of the approximate
problem are also those of the original problem. Using exact penalty techniques
in DC programming, we proved stronger results for some particular
approximations, namely, the approximate problem, with suitable parameters, is
equivalent to the original problem. The efficiency of several sparse inducing
penalty functions have been fully analyzed. Four DCA (DC Algorithm) schemes
were developed that cover all standard algorithms in nonconvex sparse
approximation approaches as special versions. They can be viewed as, an $\ell
_{1}$-perturbed algorithm / reweighted-$\ell _{1}$ algorithm / reweighted-$\ell
_{1}$ algorithm. We offer a unifying nonconvex approximation approach, with
solid theoretical tools as well as efficient algorithms based on DC programming
and DCA, to tackle the zero-norm and sparse optimization. As an application, we
implemented our methods for the feature selection in SVM (Support Vector
Machine) problem and performed empirical comparative numerical experiments on
the proposed algorithms with various approximation functions.
Estimating complex causal effects from incomplete observational data
Authors: Juha Karvanen
Date: 2014-03-05 00:00:00
Tasks: Additive models, Imputation

Despite the major advances taken in causal modeling, causality is still an
unfamiliar topic for many statisticians. In this paper, it is demonstrated from
the beginning to the end how causal effects can be estimated from observational
data assuming that the causal structure is known. To make the problem more
challenging, the causal effects are highly nonlinear and the data are missing
at random. The tools used in the estimation include causal models with design,
causal calculus, multiple imputation and generalized additive models. The main
message is that a trained statistician can estimate causal effects by
judiciously combining existing tools.
Classification-based Approximate Policy Iteration: Experiments and Extended Discussions
Authors: Amir-Massoud Farahmand, Doina Precup, André M. S. Barreto, Mohammad Ghavamzadeh
Date: 2014-07-02 00:00:00
Tasks: Classification, General Classification, Reinforcement Learning

Tackling large approximate dynamic programming or reinforcement learning
problems requires methods that can exploit regularities, or intrinsic
structure, of the problem in hand. Most current methods are geared towards
exploiting the regularities of either the value function or the policy. We
introduce a general classification-based approximate policy iteration (CAPI)
framework, which encompasses a large class of algorithms that can exploit
regularities of both the value function and the policy space, depending on what
is advantageous. This framework has two main components: a generic value
function estimator and a classifier that learns a policy based on the estimated
value function. We establish theoretical guarantees for the sample complexity
of CAPI-style algorithms, which allow the policy evaluation step to be
performed by a wide variety of algorithms (including temporal-difference-style
methods), and can handle nonparametric representations of policies. Our bounds
on the estimation error of the performance loss are tighter than existing
results. We also illustrate this approach empirically on several problems,
including a large HIV control task.
Rates of Convergence for Nearest Neighbor Classification
Authors: Kamalika Chaudhuri, Sanjoy Dasgupta
Date: 2014-06-30 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Classification, General Classification

Nearest neighbor methods are a popular class of nonparametric estimators with
several desirable properties, such as adaptivity to different distance scales
in different regions of space. Prior work on convergence rates for nearest
neighbor classification has not fully reflected these subtle properties. We
analyze the behavior of these estimators in metric spaces and provide
finite-sample, distribution-dependent rates of convergence under minimal
assumptions. As a by-product, we are able to establish the universal
consistency of nearest neighbor in a broader range of data spaces than was
previously known. We illustrate our upper and lower bounds by introducing
smoothness classes that are customized for nearest neighbor classification.
A Dynamic Simulation-Optimization Model for Adaptive Management of Urban Water Distribution System Contamination Threats
Authors: Amin Rasekh, Kelly Brumbelow
Date: 2014-07-01 00:00:00
Tasks: Management

Urban water distribution systems hold a critical and strategic position in
preserving public health and industrial growth. Despite the ubiquity of these
urban systems, aging infrastructure, and increased risk of terrorism, decision
support models for a timely and adaptive contamination emergency response still
remain at an undeveloped stage. Emergency response is characterized as a
progressive, interactive, and adaptive process that involves parallel
activities of processing streaming information and executing response actions.
This study develops a dynamic decision support model that adaptively simulates
the time-varying emergency environment and tracks changing best health
protection response measures at every stage of an emergency in real-time.
Feedback mechanisms between the contaminated network, emergency managers, and
consumers are incorporated in a dynamic simulation model to capture
time-varying characteristics of an emergency environment. An
evolutionary-computation-based dynamic optimization model is developed to
adaptively identify time-dependant optimal health protection measures during an
emergency. This dynamic simulation-optimization model treats perceived
contaminant source attributes as time-varying parameters to account for
perceived contamination source updates as more data stream in over time.
Performance of the developed dynamic decision support model is analyzed and
demonstrated using a mid-size virtual city that resembles the dynamics and
complexity of real-world urban systems. This adaptive emergency response
optimization model is intended to be a major component of an all-inclusive
cyberinfrastructure for efficient contamination threat management, which is
currently under development.
copulaedas: An R Package for Estimation of Distribution Algorithms Based on Copulas
Authors: Yasser Gonzalez-Fernandez, Marta Soto
Date: 2012-09-24 00:00:00

The use of copula-based models in EDAs (estimation of distribution
algorithms) is currently an active area of research. In this context, the
copulaedas package for R provides a platform where EDAs based on copulas can be
implemented and studied. The package offers complete implementations of various
EDAs based on copulas and vines, a group of well-known optimization problems,
and utility functions to study the performance of the algorithms. Newly
developed EDAs can be easily integrated into the package by extending an S4
class with generic functions for their main components. This paper presents
copulaedas by providing an overview of EDAs based on copulas, a description of
the implementation of the package, and an illustration of its use through
examples. The examples include running the EDAs defined in the package,
implementing new algorithms, and performing an empirical study to compare the
behavior of different algorithms on benchmark functions and a real-world
problem.