pwc-archive/papers-with-abstracts huggingface dataset:
Planning for Decentralized Control of Multiple Robots Under Uncertainty
Authors: Christopher Amato, George D. Konidaris, Gabriel Cruz, Christopher A. Maynor, Jonathan P. How, Leslie P. Kaelbling
Date: 2014-02-12 00:00:00

We describe a probabilistic framework for synthesizing control policies for
general multi-robot systems, given environment and sensor models and a cost
function. Decentralized, partially observable Markov decision processes
(Dec-POMDPs) are a general model of decision processes where a team of agents
must cooperate to optimize some objective (specified by a shared reward or cost
function) in the presence of uncertainty, but where communication limitations
mean that the agents cannot share their state, so execution must proceed in a
decentralized fashion. While Dec-POMDPs are typically intractable to solve for
real-world problems, recent research on the use of macro-actions in Dec-POMDPs
has significantly increased the size of problem that can be practically solved
as a Dec-POMDP. We describe this general model, and show how, in contrast to
most existing methods that are specialized to a particular problem class, it
can synthesize control policies that use whatever opportunities for
coordination are present in the problem, while balancing off uncertainty in
outcomes, sensor information, and information about other agents. We use three
variations on a warehouse task to show that a single planner of this type can
generate cooperative behavior using task allocation, direct communication, and
signaling, as appropriate.
Probabilistic Solutions to Differential Equations and their Application to Riemannian Statistics
Authors: Philipp Hennig, Søren Hauberg
Date: 2013-06-03 00:00:00

We study a probabilistic numerical method for the solution of both boundary
and initial value problems that returns a joint Gaussian process posterior over
the solution. Such methods have concrete value in the statistics on Riemannian
manifolds, where non-analytic ordinary differential equations are involved in
virtually all computations. The probabilistic formulation permits marginalising
the uncertainty of the numerical solution such that statistics are less
sensitive to inaccuracies. This leads to new Riemannian algorithms for mean
value computations and principal geodesic analysis. Marginalisation also means
results can be less precise than point estimates, enabling a noticeable
speed-up over the state of the art. Our approach is an argument for a wider
point that uncertainty caused by numerical calculations should be tracked
throughout the pipeline of machine learning algorithms.
PR2: A Language Independent Unsupervised Tool for Personality Recognition from Text
Authors: Fabio Celli, Massimo Poesio
Date: 2014-02-12 00:00:00
Tasks: General Classification

We present PR2, a personality recognition system available online, that
performs instance-based classification of Big5 personality types from
unstructured text, using language-independent features. It has been tested on
English and Italian, achieving performances up to f=.68.
Ellipsoidal Rounding for Nonnegative Matrix Factorization Under Noisy Separability
Authors: Tomohiko Mizutani
Date: 2013-09-23 00:00:00
Tasks: Clustering

We present a numerical algorithm for nonnegative matrix factorization (NMF)
problems under noisy separability. An NMF problem under separability can be
stated as one of finding all vertices of the convex hull of data points. The
research interest of this paper is to find the vectors as close to the vertices
as possible in a situation in which noise is added to the data points. Our
algorithm is designed to capture the shape of the convex hull of data points by
using its enclosing ellipsoid. We show that the algorithm has correctness and
robustness properties from theoretical and practical perspectives; correctness
here means that if the data points do not contain any noise, the algorithm can
find the vertices of their convex hull; robustness means that if the data
points contain noise, the algorithm can find the near-vertices. Finally, we
apply the algorithm to document clustering, and report the experimental
results.
Relaxed Sparse Eigenvalue Conditions for Sparse Estimation via Non-convex Regularized Regression
Authors: Zheng Pan, Chang-Shui Zhang
Date: 2013-06-14 00:00:00
Tasks: parameter estimation, regression

Non-convex regularizers usually improve the performance of sparse estimation
in practice. To prove this fact, we study the conditions of sparse estimations
for the sharp concave regularizers which are a general family of non-convex
regularizers including many existing regularizers. For the global solutions of
the regularized regression, our sparse eigenvalue based conditions are weaker
than that of L1-regularization for parameter estimation and sparseness
estimation. For the approximate global and approximate stationary (AGAS)
solutions, almost the same conditions are also enough. We show that the desired
AGAS solutions can be obtained by coordinate descent (CD) based methods.
Finally, we perform some experiments to show the performance of CD methods on
giving AGAS solutions and the degree of weakness of the estimation conditions
required by the sharp concave regularizers.
Multi-Task Policy Search
Authors: Marc Peter Deisenroth, Peter Englert, Jan Peters, Dieter Fox
Date: 2013-07-02 00:00:00
Tasks: Imitation Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Learning policies that generalize across multiple tasks is an important and
challenging research topic in reinforcement learning and robotics. Training
individual policies for every single potential task is often impractical,
especially for continuous task variations, requiring more principled approaches
to share and transfer knowledge among similar tasks. We present a novel
approach for learning a nonlinear feedback policy that generalizes across
multiple tasks. The key idea is to define a parametrized policy as a function
of both the state and the task, which allows learning a single policy that
generalizes across multiple known and unknown tasks. Applications of our novel
approach to reinforcement and imitation learning in real-robot experiments are
shown.
A Survey on Metric Learning for Feature Vectors and Structured Data
Authors: Aurélien Bellet, Amaury Habrard, Marc Sebban
Date: 2013-06-28 00:00:00
Tasks: BIG-bench Machine Learning, Metric Learning, Survey

The need for appropriate ways to measure the distance or similarity between
data is ubiquitous in machine learning, pattern recognition and data mining,
but handcrafting such good metrics for specific problems is generally
difficult. This has led to the emergence of metric learning, which aims at
automatically learning a metric from data and has attracted a lot of interest
in machine learning and related fields for the past ten years. This survey
paper proposes a systematic review of the metric learning literature,
highlighting the pros and cons of each approach. We pay particular attention to
Mahalanobis distance metric learning, a well-studied and successful framework,
but additionally present a wide range of methods that have recently emerged as
powerful alternatives, including nonlinear metric learning, similarity learning
and local metric learning. Recent trends and extensions, such as
semi-supervised metric learning, metric learning for histogram data and the
derivation of generalization guarantees, are also covered. Finally, this survey
addresses metric learning for structured data, in particular edit distance
learning, and attempts to give an overview of the remaining challenges in
metric learning for the years to come.
Bayesian Inference with Posterior Regularization and applications to Infinite Latent SVMs
Authors: Jun Zhu, Ning Chen, Eric P. Xing
Date: 2012-10-05 00:00:00
Tasks: Bayesian Inference, Multi-Task Learning

Existing Bayesian models, especially nonparametric Bayesian methods, rely on
specially conceived priors to incorporate domain knowledge for discovering
improved latent representations. While priors can affect posterior
distributions through Bayes' rule, imposing posterior regularization is
arguably more direct and in some cases more natural and general. In this paper,
we present regularized Bayesian inference (RegBayes), a novel computational
framework that performs posterior inference with a regularization term on the
desired post-data posterior distribution under an information theoretical
formulation. RegBayes is more flexible than the procedure that elicits expert
knowledge via priors, and it covers both directed Bayesian networks and
undirected Markov networks whose Bayesian formulation results in hybrid chain
graph models. When the regularization is induced from a linear operator on the
posterior distributions, such as the expectation operator, we present a general
convex-analysis theorem to characterize the solution of RegBayes. Furthermore,
we present two concrete examples of RegBayes, infinite latent support vector
machines (iLSVM) and multi-task infinite latent support vector machines
(MT-iLSVM), which explore the large-margin idea in combination with a
nonparametric Bayesian model for discovering predictive latent features for
classification and multi-task learning, respectively. We present efficient
inference methods and report empirical studies on several benchmark datasets,
which appear to demonstrate the merits inherited from both large-margin
learning and Bayesian nonparametrics. Such results were not available until
now, and contribute to push forward the interface between these two important
subfields, which have been largely treated as isolated in the community.
Noise Analysis for Lensless Compressive Imaging
Authors: Hong Jiang, Gang Huang, Paul Wilford
Date: 2014-02-12 00:00:00
Tasks: Compressive Sensing

We analyze the signal to noise ratio (SNR) in a recently proposed lensless
compressive imaging architecture. The architecture consists of a sensor of a
single detector element and an aperture assembly of an array of aperture
elements, each of which has a programmable transmittance. This lensless
compressive imaging architecture can be used in conjunction with compressive
sensing to capture images in a compressed form of compressive measurements. In
this paper, we perform noise analysis of this lensless compressive imaging
architecture and compare it with pinhole aperture imaging and lens aperture
imaging. We will show that the SNR in the lensless compressive imaging is
independent of the image resolution, while that in either pinhole aperture
imaging or lens aperture imaging decreases as the image resolution increases.
Consequently, the SNR in the lensless compressive imaging can be much higher if
the image resolution is large enough.
Sex as Gibbs Sampling: a probability model of evolution
Authors: Chris Watkins, Yvonne Buttkewitz
Date: 2014-02-12 00:00:00

We show that evolutionary computation can be implemented as standard
Markov-chain Monte-Carlo (MCMC) sampling. With some care, `genetic algorithms'
can be constructed that are reversible Markov chains that satisfy detailed
balance; it follows that the stationary distribution of populations is a Gibbs
distribution in a simple factorised form. For some standard and popular
nonparametric probability models, we exhibit Gibbs-sampling procedures that are
plausible genetic algorithms. At mutation-selection equilibrium, a population
of genomes is analogous to a sample from a Bayesian posterior, and the genomes
are analogous to latent variables. We suggest this is a general, tractable, and
insightful formulation of evolutionary computation in terms of standard machine
learning concepts and techniques.
  In addition, we show that evolutionary processes in which selection acts by
differences in fecundity are not reversible, and also that it is not possible
to construct reversible evolutionary models in which each child is produced by
only two parents.
An Extensive Experimental Study on the Cluster-based Reference Set Reduction for speeding-up the k-NN Classifier
Authors: Stefanos Ougiaroglou, Georgios Evangelidis, Dimitris A. Dervos
Date: 2013-09-30 00:00:00
Tasks: General Classification

The k-Nearest Neighbor (k-NN) classification algorithm is one of the most
widely-used lazy classifiers because of its simplicity and ease of
implementation. It is considered to be an effective classifier and has many
applications. However, its major drawback is that when sequential search is
used to find the neighbors, it involves high computational cost. Speeding-up
k-NN search is still an active research field. Hwang and Cho have recently
proposed an adaptive cluster-based method for fast Nearest Neighbor searching.
The effectiveness of this method is based on the adjustment of three
parameters. However, the authors evaluated their method by setting specific
parameter values and using only one dataset. In this paper, an extensive
experimental study of this method is presented. The results, which are based on
five real life datasets, illustrate that if the parameters of the method are
carefully defined, one can achieve even better classification performance.
Real-Time Hand Shape Classification
Authors: Jakub Nalepa, Michal Kawulok
Date: 2014-02-11 00:00:00
Tasks: Classification, General Classification

The problem of hand shape classification is challenging since a hand is
characterized by a large number of degrees of freedom. Numerous shape
descriptors have been proposed and applied over the years to estimate and
classify hand poses in reasonable time. In this paper we discuss our parallel
framework for real-time hand shape classification applicable in real-time
applications. We show how the number of gallery images influences the
classification accuracy and execution time of the parallel algorithm. We
present the speedup and efficiency analyses that prove the efficacy of the
parallel implementation. Noteworthy, different methods can be used at each step
of our parallel framework. Here, we combine the shape contexts with the
appearance-based techniques to enhance the robustness of the algorithm and to
increase the classification score. An extensive experimental study proves the
superiority of the proposed approach over existing state-of-the-art methods.
On Zeroth-Order Stochastic Convex Optimization via Random Walks
Authors: Tengyuan Liang, Hariharan Narayanan, Alexander Rakhlin
Date: 2014-02-11 00:00:00

We propose a method for zeroth order stochastic convex optimization that
attains the suboptimality rate of $\tilde{\mathcal{O}}(n^{7}T^{-1/2})$ after
$T$ queries for a convex bounded function $f:{\mathbb R}^n\to{\mathbb R}$. The
method is based on a random walk (the \emph{Ball Walk}) on the epigraph of the
function. The randomized approach circumvents the problem of gradient
estimation, and appears to be less sensitive to noisy function evaluations
compared to noiseless zeroth order methods.
A Fast Two Pass Multi-Value Segmentation Algorithm based on Connected Component Analysis
Authors: Dibyendu Mukherjee
Date: 2014-02-11 00:00:00
Tasks: Segmentation, Video Segmentation, Video Semantic Segmentation

Connected component analysis (CCA) has been heavily used to label binary
images and classify segments. However, it has not been well-exploited to
segment multi-valued natural images. This work proposes a novel multi-value
segmentation algorithm that utilizes CCA to segment color images. A user
defined distance measure is incorporated in the proposed modified CCA to
identify and segment similar image regions. The raw output of the algorithm
consists of distinctly labelled segmented regions. The proposed algorithm has a
unique design architecture that provides several benefits: 1) it can be used to
segment any multi-channel multi-valued image; 2) the distance
measure/segmentation criteria can be application-specific and 3) an absolute
linear-time implementation allows easy extension for real-time video
segmentation. Experimental demonstrations of the aforesaid benefits are
presented along with the comparison results on multiple datasets with current
benchmark algorithms. A number of possible application areas are also
identified and results on real-time video segmentation has been presented to
show the promise of the proposed method.
Online Nonparametric Regression
Authors: Alexander Rakhlin, Karthik Sridharan
Date: 2014-02-11 00:00:00
Tasks: regression

We establish optimal rates for online regression for arbitrary classes of
regression functions in terms of the sequential entropy introduced in (Rakhlin,
Sridharan, Tewari, 2010). The optimal rates are shown to exhibit a phase
transition analogous to the i.i.d./statistical learning case, studied in
(Rakhlin, Sridharan, Tsybakov 2013). In the frequently encountered situation
when sequential entropy and i.i.d. empirical entropy match, our results point
to the interesting phenomenon that the rates for statistical learning with
squared loss and online nonparametric regression are the same.
  In addition to a non-algorithmic study of minimax regret, we exhibit a
generic forecaster that enjoys the established optimal rates. We also provide a
recipe for designing online regression algorithms that can be computationally
efficient. We illustrate the techniques by deriving existing and new
forecasters for the case of finite experts and for online linear regression.
Sequentially Generated Instance-Dependent Image Representations for Classification
Authors: Gabriel Dulac-Arnold, Ludovic Denoyer, Nicolas Thome, Matthieu Cord, Patrick Gallinari
Date: 2013-12-20 00:00:00
Tasks: Classification, General Classification, image-classification, Image Classification

In this paper, we investigate a new framework for image classification that
adaptively generates spatial representations. Our strategy is based on a
sequential process that learns to explore the different regions of any image in
order to infer its category. In particular, the choice of regions is specific
to each image, directed by the actual content of previously selected
regions.The capacity of the system to handle incomplete image information as
well as its adaptive region selection allow the system to perform well in
budgeted classification tasks by exploiting a dynamicly generated
representation of each image. We demonstrate the system's abilities in a series
of image-based exploration and classification tasks that highlight its learned
exploration and inference abilities.
Realtime Multilevel Crowd Tracking using Reciprocal Velocity Obstacles
Authors: Aniket Bera, Dinesh Manocha
Date: 2014-02-11 00:00:00
Tasks: CPU

We present a novel, realtime algorithm to compute the trajectory of each
pedestrian in moderately dense crowd scenes. Our formulation is based on an
adaptive particle filtering scheme that uses a multi-agent motion model based
on velocity-obstacles, and takes into account local interactions as well as
physical and personal constraints of each pedestrian. Our method dynamically
changes the number of particles allocated to each pedestrian based on different
confidence metrics. Additionally, we use a new high-definition crowd video
dataset, which is used to evaluate the performance of different pedestrian
tracking algorithms. This dataset consists of videos of indoor and outdoor
scenes, recorded at different locations with 30-80 pedestrians. We highlight
the performance benefits of our algorithm over prior techniques using this
dataset. In practice, our algorithm can compute trajectories of tens of
pedestrians on a multi-core desktop CPU at interactive rates (27-30 frames per
second). To the best of our knowledge, our approach is 4-5 times faster than
prior methods, which provide similar accuracy.
Justifying Information-Geometric Causal Inference
Authors: Dominik Janzing, Bastian Steudel, Naji Shajarisales, Bernhard Schölkopf
Date: 2014-02-11 00:00:00
Tasks: Causal Inference

Information Geometric Causal Inference (IGCI) is a new approach to
distinguish between cause and effect for two variables. It is based on an
independence assumption between input distribution and causal mechanism that
can be phrased in terms of orthogonality in information space. We describe two
intuitive reinterpretations of this approach that makes IGCI more accessible to
a broader audience.
  Moreover, we show that the described independence is related to the
hypothesis that unsupervised learning and semi-supervised learning only works
for predicting the cause from the effect and not vice versa.
An evaluation of keyword extraction from online communication for the characterisation of social relations
Authors: Jan Hauffa, Tobias Lichtenberg, Georg Groh
Date: 2014-02-11 00:00:00
Tasks: Keyword Extraction

The set of interpersonal relationships on a social network service or a
similar online community is usually highly heterogenous. The concept of tie
strength captures only one aspect of this heterogeneity. Since the unstructured
text content of online communication artefacts is a salient source of
information about a social relationship, we investigate the utility of keywords
extracted from the message body as a representation of the relationship's
characteristics as reflected by the conversation topics. Keyword extraction is
performed using standard natural language processing methods. Communication
data and human assessments of the extracted keywords are obtained from Facebook
users via a custom application. The overall positive quality assessment
provides evidence that the keywords indeed convey relevant information about
the relationship.
Imaging with Rays: Microscopy, Medical Imaging, and Computer Vision
Authors: Keith Dillon, Yeshaiahu Fainman
Date: 2014-02-11 00:00:00
Tasks: Object

In this paper we broadly consider techniques which utilize projections on
rays for data collection, with particular emphasis on optical techniques. We
formulate a variety of imaging techniques as either special cases or extensions
of tomographic reconstruction. We then consider how the techniques must be
extended to describe objects containing occlusion, as with a self-occluding
opaque object. We formulate the reconstruction problem as a regularized
nonlinear optimization problem to simultaneously solve for object brightness
and attenuation, where the attenuation can become infinite. We demonstrate
various simulated examples for imaging opaque objects, including sparse point
sources, a conventional multiview reconstruction technique, and a
super-resolving technique which exploits occlusion to resolve an image.
Kernel Least Mean Square with Adaptive Kernel Size
Authors: Badong Chen, Junli Liang, Nanning Zheng, Jose C. Principe
Date: 2014-01-23 00:00:00
Tasks: Time Series, Time Series Analysis, Time Series Prediction

Kernel adaptive filters (KAF) are a class of powerful nonlinear filters
developed in Reproducing Kernel Hilbert Space (RKHS). The Gaussian kernel is
usually the default kernel in KAF algorithms, but selecting the proper kernel
size (bandwidth) is still an open important issue especially for learning with
small sample sizes. In previous research, the kernel size was set manually or
estimated in advance by Silvermans rule based on the sample distribution. This
study aims to develop an online technique for optimizing the kernel size of the
kernel least mean square (KLMS) algorithm. A sequential optimization strategy
is proposed, and a new algorithm is developed, in which the filter weights and
the kernel size are both sequentially updated by stochastic gradient algorithms
that minimize the mean square error (MSE). Theoretical results on convergence
are also presented. The excellent performance of the new algorithm is confirmed
by simulations on static function estimation and short term chaotic time series
prediction.