pwc-archive/papers-with-abstracts huggingface dataset:
Perturbed Gibbs Samplers for Synthetic Data Release
Authors: Yubin Park, Joydeep Ghosh
Date: 2013-12-18 00:00:00
Tasks: Imputation, regression

We propose a categorical data synthesizer with a quantifiable disclosure
risk. Our algorithm, named Perturbed Gibbs Sampler, can handle high-dimensional
categorical data that are often intractable to represent as contingency tables.
The algorithm extends a multiple imputation strategy for fully synthetic data
by utilizing feature hashing and non-parametric distribution approximations.
California Patient Discharge data are used to demonstrate statistical
properties of the proposed synthesizing methodology. Marginal and conditional
distributions, as well as the coefficients of regression models built on the
synthesized data are compared to those obtained from the original data.
Intruder scenarios are simulated to evaluate disclosure risks of the
synthesized data from multiple angles. Limitations and extensions of the
proposed algorithm are also discussed.
Generative NeuroEvolution for Deep Learning
Authors: Phillip Verbancsics, Josh Harguess
Date: 2013-12-18 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, image-classification, Image Classification

An important goal for the machine learning (ML) community is to create
approaches that can learn solutions with human-level capability. One domain
where humans have held a significant advantage is visual processing. A
significant approach to addressing this gap has been machine learning
approaches that are inspired from the natural systems, such as artificial
neural networks (ANNs), evolutionary computation (EC), and generative and
developmental systems (GDS). Research into deep learning has demonstrated that
such architectures can achieve performance competitive with humans on some
visual tasks; however, these systems have been primarily trained through
supervised and unsupervised learning algorithms. Alternatively, research is
showing that evolution may have a significant role in the development of visual
systems. Thus this paper investigates the role neuro-evolution (NE) can take in
deep learning. In particular, the Hypercube-based NeuroEvolution of Augmenting
Topologies is a NE approach that can effectively learn large neural structures
by training an indirect encoding that compresses the ANN weight pattern as a
function of geometry. The results show that HyperNEAT struggles with performing
image classification by itself, but can be effective in training a feature
extractor that other ML approaches can learn from. Thus NeuroEvolution combined
with other ML methods provides an intriguing area of research that can
replicate the processes in nature.
SOMz: photometric redshift PDFs with self organizing maps and random atlas
Authors: M. Carrasco Kind, R. J. Brunner
Date: 2013-12-18 00:00:00
Tasks: BIG-bench Machine Learning

In this paper we explore the applicability of the unsupervised machine
learning technique of Self Organizing Maps (SOM) to estimate galaxy photometric
redshift probability density functions (PDFs). This technique takes a
spectroscopic training set, and maps the photometric attributes, but not the
redshifts, to a two dimensional surface by using a process of competitive
learning where neurons compete to more closely resemble the training data
multidimensional space. The key feature of a SOM is that it retains the
topology of the input set, revealing correlations between the attributes that
are not easily identified. We test three different 2D topological mapping:
rectangular, hexagonal, and spherical, by using data from the DEEP2 survey. We
also explore different implementations and boundary conditions on the map and
also introduce the idea of a random atlas where a large number of different
maps are created and their individual predictions are aggregated to produce a
more robust photometric redshift PDF. We also introduced a new metric, the
$I$-score, which efficiently incorporates different metrics, making it easier
to compare different results (from different parameters or different
photometric redshift codes). We find that by using a spherical topology mapping
we obtain a better representation of the underlying multidimensional topology,
which provides more accurate results that are comparable to other,
state-of-the-art machine learning algorithms. Our results illustrate that
unsupervised approaches have great potential for many astronomical problems,
and in particular for the computation of photometric redshifts.
Systematic and multifactor risk models revisited
Authors: Michel Fliess, Cédric Join
Date: 2013-12-18 00:00:00

Systematic and multifactor risk models are revisited via methods which were
already successfully developed in signal processing and in automatic control.
The results, which bypass the usual criticisms on those risk modeling, are
illustrated by several successful computer experiments.
Shape Tracking With Occlusions via Coarse-To-Fine Region-Based Sobolev Descent
Authors: Yanchao Yang, Ganesh Sundaramoorthi
Date: 2012-08-21 00:00:00
Tasks: Object, Object Tracking

We present a method to track the precise shape of an object in video based on
new modeling and optimization on a new Riemannian manifold of parameterized
regions.
  Joint dynamic shape and appearance models, in which a template of the object
is propagated to match the object shape and radiance in the next frame, are
advantageous over methods employing global image statistics in cases of complex
object radiance and cluttered background. In cases of 3D object motion and
viewpoint change, self-occlusions and dis-occlusions of the object are
prominent, and current methods employing joint shape and appearance models are
unable to adapt to new shape and appearance information, leading to inaccurate
shape detection. In this work, we model self-occlusions and dis-occlusions in a
joint shape and appearance tracking framework.
  Self-occlusions and the warp to propagate the template are coupled, thus a
joint problem is formulated. We derive a coarse-to-fine optimization scheme,
advantageous in object tracking, that initially perturbs the template by coarse
perturbations before transitioning to finer-scale perturbations, traversing all
scales, seamlessly and automatically. The scheme is a gradient descent on a
novel infinite-dimensional Riemannian manifold that we introduce. The manifold
consists of planar parameterized regions, and the metric that we introduce is a
novel Sobolev-type metric defined on infinitesimal vector fields on regions.
The metric has the property of resulting in a gradient descent that
automatically favors coarse-scale deformations (when they reduce the energy)
before moving to finer-scale deformations.
  Experiments on video exhibiting occlusion/dis-occlusion, complex radiance and
background show that occlusion/dis-occlusion modeling leads to superior shape
accuracy compared to recent methods employing joint shape/appearance models or
employing global statistics.
A U-statistic estimator for the variance of resampling-based error estimators
Authors: Mathias Fuchs, Roman Hornung, Riccardo De Bin, Anne-Laure Boulesteix
Date: 2013-10-30 00:00:00
Tasks: Binary Classification, General Classification

We revisit resampling procedures for error estimation in binary
classification in terms of U-statistics. In particular, we exploit the fact
that the error rate estimator involving all learning-testing splits is a
U-statistic. Thus, it has minimal variance among all unbiased estimators and is
asymptotically normally distributed. Moreover, there is an unbiased estimator
for this minimal variance if the total sample size is at least the double
learning set size plus two. In this case, we exhibit such an estimator which is
another U-statistic. It enjoys, again, various optimality properties and yields
an asymptotically exact hypothesis test of the equality of error rates when two
learning algorithms are compared. Our statements apply to any deterministic
learning algorithms under weak non-degeneracy assumptions.
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited
Authors: Matthias Hein, Simon Setzer, Leonardo Jost, Syama Sundar Rangapuram
Date: 2013-12-18 00:00:00
Proceeding: NeurIPS 2013 12

Hypergraphs allow one to encode higher-order relationships in data and are
thus a very flexible modeling tool. Current learning methods are either based
on approximations of the hypergraphs via graphs or on tensor methods which are
only applicable under special conditions. In this paper, we present a new
learning framework on hypergraphs which fully uses the hypergraph structure.
The key element is a family of regularization functionals based on the total
variation on hypergraphs.
Permuted NMF: A Simple Algorithm Intended to Minimize the Volume of the Score Matrix
Authors: Paul Fogel
Date: 2013-12-18 00:00:00

Non-Negative Matrix Factorization, NMF, attempts to find a number of
archetypal response profiles, or parts, such that any sample profile in the
dataset can be approximated by a close profile among these archetypes or a
linear combination of these profiles. The non-negativity constraint is imposed
while estimating archetypal profiles, due to the non-negative nature of the
observed signal. Apart from non negativity, a volume constraint can be applied
on the Score matrix W to enhance the ability of learning parts of NMF. In this
report, we describe a very simple algorithm, which in effect achieves volume
minimization, although indirectly.
Efficient Bayes-Adaptive Reinforcement Learning using Sample-Based Search
Authors: Arthur Guez, David Silver, Peter Dayan
Date: 2012-05-14 00:00:00
Proceeding: NeurIPS 2012 12
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Bayesian model-based reinforcement learning is a formally elegant approach to
learning optimal behaviour under model uncertainty, trading off exploration and
exploitation in an ideal way. Unfortunately, finding the resulting
Bayes-optimal policies is notoriously taxing, since the search space becomes
enormous. In this paper we introduce a tractable, sample-based method for
approximate Bayes-optimal planning which exploits Monte-Carlo tree search. Our
approach outperformed prior Bayesian model-based RL algorithms by a significant
margin on several well-known benchmark problems -- because it avoids expensive
applications of Bayes rule within the search tree by lazily sampling models
from the current beliefs. We illustrate the advantages of our approach by
showing it working in an infinite state space domain which is qualitatively out
of reach of almost all previous work in Bayesian exploration.
A Cellular Automaton Based Controller for a Ms. Pac-Man Agent
Authors: Alexander Darer, Peter Lewis
Date: 2013-12-18 00:00:00

Video games can be used as an excellent test bed for Artificial Intelligence
(AI) techniques. They are challenging and non-deterministic, this makes it very
difficult to write strong AI players. An example of such a video game is Ms.
Pac-Man. In this paper we will outline some of the previous techniques used to
build AI controllers for Ms. Pac-Man as well as presenting a new and novel
solution. My technique utilises a Cellular Automaton (CA) to build a
representation of the environment at each time step of the game. The basis of
the representation is a 2-D grid of cells. Each cell has a state and a set of
rules which determine whether or not that cell will dominate (i.e. pass its
state value onto) adjacent cells at each update. Once a certain number of
update iterations have been completed, the CA represents the state of the
environment in the game, the goals and the dangers. At this point, Ms. Pac-Man
will decide her next move based only on her adjacent cells, that is to say, she
has no knowledge of the state of the environment as a whole, she will simply
follow the strongest path. This technique shows promise and allows the
controller to achieve high scores in a live game, the behaviour it exhibits is
interesting and complex. Moreover, this behaviour is produced by using very
simple rules which are applied many times to each cell in the grid. Simple
local interactions with complex global results are truly achieved.
Functional Bipartite Ranking: a Wavelet-Based Filtering Approach
Authors: Stéphan Clémençon, Marine Depecker
Date: 2013-12-18 00:00:00

It is the main goal of this article to address the bipartite ranking issue
from the perspective of functional data analysis (FDA). Given a training set of
independent realizations of a (possibly sampled) second-order random function
with a (locally) smooth autocorrelation structure and to which a binary label
is randomly assigned, the objective is to learn a scoring function s with
optimal ROC curve. Based on linear/nonlinear wavelet-based approximations, it
is shown how to select compact finite dimensional representations of the input
curves adaptively, in order to build accurate ranking rules, using recent
advances in the ranking problem for multivariate data with binary feedback.
Beyond theoretical considerations, the performance of the learning methods for
functional bipartite ranking proposed in this paper are illustrated by
numerical experiments.
Comparative analysis of evolutionary algorithms for image enhancement
Authors: Anupriya Gogna, Akash Tayal
Date: 2013-12-18 00:00:00
Tasks: Evolutionary Algorithms, Image Enhancement

Evolutionary algorithms are metaheuristic techniques that derive inspiration
from the natural process of evolution. They can efficiently solve (generate
acceptable quality of solution in reasonable time) complex optimization
(NP-Hard) problems. In this paper, automatic image enhancement is considered as
an optimization problem and three evolutionary algorithms (Genetic Algorithm,
Differential Evolution and Self Organizing Migration Algorithm) are employed to
search for an optimum solution. They are used to find an optimum parameter set
for an image enhancement transfer function. The aim is to maximize a fitness
criterion which is a measure of image contrast and the visibility of details in
the enhanced image. The enhancement results obtained using all three
evolutionary algorithms are compared amongst themselves and also with the
output of histogram equalization method.
Evaluation of Plane Detection with RANSAC According to Density of 3D Point Clouds
Authors: Tomofumi Fujiwara, Tetsushi Kamegawa, Akio Gofuku
Date: 2013-12-18 00:00:00

We have implemented a method that detects planar regions from 3D scan data
using Random Sample Consensus (RANSAC) algorithm to address the issue of a
trade-off between the scanning speed and the point density of 3D scanning.
However, the limitation of the implemented method has not been clear yet. In
this paper, we conducted an additional experiment to evaluate the implemented
method by changing its parameter and environments in both high and low point
density data. As a result, the number of detected planes in high point density
data was different from that in low point density data with the same parameter
value.
Contextually Supervised Source Separation with Application to Energy Disaggregation
Authors: Matt Wytock, J. Zico Kolter
Date: 2013-12-18 00:00:00

We propose a new framework for single-channel source separation that lies
between the fully supervised and unsupervised setting. Instead of supervision,
we provide input features for each source signal and use convex methods to
estimate the correlations between these features and the unobserved signal
decomposition. We analyze the case of $\ell_2$ loss theoretically and show that
recovery of the signal components depends only on cross-correlation between
features for different signals, not on correlations between features for the
same signal. Contextually supervised source separation is a natural fit for
domains with large amounts of data but no explicit supervision; our motivating
application is energy disaggregation of hourly smart meter data (the separation
of whole-home power signals into different energy uses). Here we apply
contextual supervision to disaggregate the energy usage of thousands homes over
four years, a significantly larger scale than previously published efforts, and
demonstrate on synthetic data that our method outperforms the unsupervised
approach.
Efficient Online Bootstrapping for Large Scale Learning
Authors: Zhen Qin, Vaclav Petricek, Nikos Karampatziakis, Lihong Li, John Langford
Date: 2013-12-18 00:00:00
Tasks: Prediction

Bootstrapping is a useful technique for estimating the uncertainty of a
predictor, for example, confidence intervals for prediction. It is typically
used on small to moderate sized datasets, due to its high computation cost.
This work describes a highly scalable online bootstrapping strategy,
implemented inside Vowpal Wabbit, that is several times faster than traditional
strategies. Our experiments indicate that, in addition to providing a black
box-like method for estimating uncertainty, our implementation of online
bootstrapping may also help to train models with better prediction performance
due to model averaging.
A Comparative Evaluation of Curriculum Learning with Filtering and Boosting
Authors: Michael R. Smith, Tony Martinez
Date: 2013-12-17 00:00:00
Tasks: General Classification

Not all instances in a data set are equally beneficial for inferring a model
of the data. Some instances (such as outliers) are detrimental to inferring a
model of the data. Several machine learning techniques treat instances in a
data set differently during training such as curriculum learning, filtering,
and boosting. However, an automated method for determining how beneficial an
instance is for inferring a model of the data does not exist. In this paper, we
present an automated method that orders the instances in a data set by
complexity based on the their likelihood of being misclassified (instance
hardness). The underlying assumption of this method is that instances with a
high likelihood of being misclassified represent more complex concepts in a
data set. Ordering the instances in a data set allows a learning algorithm to
focus on the most beneficial instances and ignore the detrimental ones. We
compare ordering the instances in a data set in curriculum learning, filtering
and boosting. We find that ordering the instances significantly increases
classification accuracy and that filtering has the largest impact on
classification accuracy. On a set of 52 data sets, ordering the instances
increases the average accuracy from 81% to 84%.
Recursive Compressed Sensing
Authors: Nikolaos M. Freris, Orhan Öçal, Martin Vetterli
Date: 2013-12-17 00:00:00
Tasks: compressed sensing

We introduce a recursive algorithm for performing compressed sensing on
streaming data. The approach consists of a) recursive encoding, where we sample
the input stream via overlapping windowing and make use of the previous
measurement in obtaining the next one, and b) recursive decoding, where the
signal estimate from the previous window is utilized in order to achieve faster
convergence in an iterative optimization scheme applied to decode the new one.
To remove estimation bias, a two-step estimation procedure is proposed
comprising support set detection and signal amplitude estimation. Estimation
accuracy is enhanced by a non-linear voting method and averaging estimates over
multiple windows. We analyze the computational complexity and estimation error,
and show that the normalized error variance asymptotically goes to zero for
sublinear sparsity. Our simulation results show speed up of an order of
magnitude over traditional CS, while obtaining significantly lower
reconstruction error under mild conditions on the signal magnitudes and the
noise level.
Sistem pendukung keputusan kelayakan TKI ke luar negeri menggunakan FMADM
Authors: Ardina Ariani, Leon Andretti Abdillah, Firamon Syakti
Date: 2013-12-17 00:00:00
Tasks: Attribute, Decision Making

BP3TKI Palembang is the government agencies that coordinate, execute and
selection of prospective migrants registration and placement. To simplify the
existing procedures and improve decision-making is necessary to build a
decision support system (DSS) to determine eligibility for employment abroad by
applying Fuzzy Multiple Attribute Decision Making (FMADM), using the linear
sequential systems development methods. The system is built using Microsoft
Visual Basic. Net 2010 and SQL Server 2008 database. The design of the system
using use case diagrams and class diagrams to identify the needs of users and
systems as well as systems implementation guidelines. This Decision Support
System able to rank and produce the prospective migrants, making it easier for
parties to take decision BP3TKI the workers who will be working out of the
country.
Identification of Gaussian Process State-Space Models with Particle Stochastic Approximation EM
Authors: Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen
Date: 2013-12-17 00:00:00
Tasks: State Space Models

Gaussian process state-space models (GP-SSMs) are a very flexible family of
models of nonlinear dynamical systems. They comprise a Bayesian nonparametric
representation of the dynamics of the system and additional (hyper-)parameters
governing the properties of this nonparametric representation. The Bayesian
formalism enables systematic reasoning about the uncertainty in the system
dynamics. We present an approach to maximum likelihood identification of the
parameters in GP-SSMs, while retaining the full nonparametric description of
the dynamics. The method is based on a stochastic approximation version of the
EM algorithm that employs recent developments in particle Markov chain Monte
Carlo for efficient identification.
Bayesian Inference and Learning in Gaussian Process State-Space Models with Particle MCMC
Authors: Roger Frigola, Fredrik Lindsten, Thomas B. Schön, Carl E. Rasmussen
Date: 2013-06-12 00:00:00
Proceeding: NeurIPS 2013 12
Tasks: Bayesian Inference, Gaussian Processes, State Estimation, State Space Models, Time Series, Time Series Analysis

State-space models are successfully used in many areas of science,
engineering and economics to model time series and dynamical systems. We
present a fully Bayesian approach to inference \emph{and learning} (i.e. state
estimation and system identification) in nonlinear nonparametric state-space
models. We place a Gaussian process prior over the state transition dynamics,
resulting in a flexible model able to capture complex dynamical phenomena. To
enable efficient inference, we marginalize over the transition dynamics
function and infer directly the joint smoothing distribution using specially
tailored Particle Markov Chain Monte Carlo samplers. Once a sample from the
smoothing distribution is computed, the state transition predictive
distribution can be formulated analytically. Our approach preserves the full
nonparametric expressivity of the model and can make use of sparse Gaussian
processes to greatly reduce computational complexity.
Matrix recovery using Split Bregman
Authors: Anupriya Gogna, Ankita Shukla, Angshul Majumdar
Date: 2013-12-17 00:00:00
Tasks: Recommendation Systems, Video Reconstruction

In this paper we address the problem of recovering a matrix, with inherent
low rank structure, from its lower dimensional projections. This problem is
frequently encountered in wide range of areas including pattern recognition,
wireless sensor networks, control systems, recommender systems, image/video
reconstruction etc. Both in theory and practice, the most optimal way to solve
the low rank matrix recovery problem is via nuclear norm minimization. In this
paper, we propose a Split Bregman algorithm for nuclear norm minimization. The
use of Bregman technique improves the convergence speed of our algorithm and
gives a higher success rate. Also, the accuracy of reconstruction is much
better even for cases where small number of linear measurements are available.
Our claim is supported by empirical results obtained using our algorithm and
its comparison to other existing methods for matrix recovery. The algorithms
are compared on the basis of NMSE, execution time and success rate for varying
ranks and sampling ratios.