pwc-archive/papers-with-abstracts huggingface dataset:
Accurate merging of images for predictive analysis using combined image
Authors: T. R. Gopalakrishnan Nair, Richa Sharma
Date: 2014-07-30 00:00:00

Several Scientific and engineering applications require merging of sampled
images for complex perception development. In most cases, for such
requirements, images are merged at intensity level. Even though it gives fairly
good perception of combined scenario of objects and scenes, it is found that
they are not sufficient enough to analyze certain engineering cases. The main
problem is incoherent modulation of intensity arising out of phase properties
being lost. In order to compensate these losses, combined phase and amplitude
merge is demanded. We present here a method which could be used in precision
engineering and biological applications where more precise prediction is
required of a combined phenomenon. When pixels are added, its original property
is lost but accurate merging of intended pixels can be achieved in high quality
using frequency domain properties of an image. This paper introduces a
technique to merge various images which can be used as a simple but effective
technique for overlapped view of a set of images and producing reduced dataset
for review purposes.
Learning Economic Parameters from Revealed Preferences
Authors: Maria-Florina Balcan, Amit Daniely, Ruta Mehta, Ruth Urner, Vijay V. Vazirani
Date: 2014-07-30 00:00:00
Tasks: Open-Ended Question Answering

A recent line of work, starting with Beigman and Vohra (2006) and
Zadimoghaddam and Roth (2012), has addressed the problem of {\em learning} a
utility function from revealed preference data. The goal here is to make use of
past data describing the purchases of a utility maximizing agent when faced
with certain prices and budget constraints in order to produce a hypothesis
function that can accurately forecast the {\em future} behavior of the agent.
  In this work we advance this line of work by providing sample complexity
guarantees and efficient algorithms for a number of important classes. By
drawing a connection to recent advances in multi-class learning, we provide a
computationally efficient algorithm with tight sample complexity guarantees
($\Theta(d/\epsilon)$ for the case of $d$ goods) for learning linear utility
functions under a linear price model. This solves an open question in
Zadimoghaddam and Roth (2012). Our technique yields numerous generalizations
including the ability to learn other well-studied classes of utility functions,
to deal with a misspecified model, and with non-linear prices.
Backwards State-space Reduction for Planning in Dynamic Knowledge Bases
Authors: Valerio Senni, Michele Stawowy
Date: 2014-07-30 00:00:00

In this paper we address the problem of planning in rich domains, where
knowledge representation is a key aspect for managing the complexity and size
of the planning domain. We follow the approach of Description Logic (DL) based
Dynamic Knowledge Bases, where a state of the world is represented concisely by
a (possibly changing) ABox and a (fixed) TBox containing the axioms, and
actions that allow to change the content of the ABox. The plan goal is given in
terms of satisfaction of a DL query. In this paper we start from a traditional
forward planning algorithm and we propose a much more efficient variant by
combining backward and forward search. In particular, we propose a Backward
State-space Reduction technique that consists in two phases: first, an Abstract
Planning Graph P is created by using the Abstract Backward Planning Algorithm
(ABP), then the abstract planning graph P is instantiated into a corresponding
planning graph P by using the Forward Plan Instantiation Algorithm (FPI). The
advantage is that in the preliminary ABP phase we produce a symbolic plan that
is a pattern to direct the search of the concrete plan. This can be seen as a
kind of informed search where the preliminary backward phase is useful to
discover properties of the state-space that can be used to direct the
subsequent forward phase. We evaluate the effectiveness of our ABP+FPI
algorithm in the reduction of the explored planning domain by comparing it to a
standard forward planning algorithm and applying both of them to a concrete
business case study.
Graph Transformation Planning via Abstraction
Authors: Steffen Ziegert
Date: 2014-07-30 00:00:00

Modern software systems increasingly incorporate self-* behavior to adapt to
changes in the environment at runtime. Such adaptations often involve
reconfiguring the software architecture of the system. Many systems also need
to manage their architecture themselves, i.e., they need a planning component
to autonomously decide which reconfigurations to execute to reach a desired
target configuration. For the specification of reconfigurations, we employ
graph transformations systems (GTS) due to the close relation of graphs and UML
object diagrams. We solve the resulting planning problems with a planning
system that works directly on a GTS. It features a domain-independent heuristic
that uses the solution length of an abstraction of the original problem as an
estimate. Finally, we provide experimental results on two different domains,
which confirm that our heuristic performs better than another
domain-independent heuristic which resembles heuristics employed in related
work.
Sequential Design for Optimal Stopping Problems
Authors: Robert B. Gramacy, Mike Ludkovski
Date: 2013-09-16 00:00:00
Tasks: Active Learning

We propose a new approach to solve optimal stopping problems via simulation.
Working within the backward dynamic programming/Snell envelope framework, we
augment the methodology of Longstaff-Schwartz that focuses on approximating the
stopping strategy. Namely, we introduce adaptive generation of the stochastic
grids anchoring the simulated sample paths of the underlying state process.
This allows for active learning of the classifiers partitioning the state space
into the continuation and stopping regions. To this end, we examine sequential
design schemes that adaptively place new design points close to the stopping
boundaries. We then discuss dynamic regression algorithms that can implement
such recursive estimation and local refinement of the classifiers. The new
algorithm is illustrated with a variety of numerical experiments, showing that
an order of magnitude savings in terms of design size can be achieved. We also
compare with existing benchmarks in the context of pricing multi-dimensional
Bermudan options.
Bayesian Probabilistic Matrix Factorization: A User Frequency Analysis
Authors: Cody Severinski, Ruslan Salakhutdinov
Date: 2014-07-29 00:00:00
Tasks: Collaborative Filtering, Variational Inference

Matrix factorization (MF) has become a common approach to collaborative
filtering, due to ease of implementation and scalability to large data sets.
Two existing drawbacks of the basic model is that it does not incorporate side
information on either users or items, and assumes a common variance for all
users. We extend the work of constrained probabilistic matrix factorization by
deriving the Gibbs updates for the side feature vectors for items
(Salakhutdinov and Minh, 2008). We show that this Bayesian treatment to the
constrained PMF model outperforms simple MAP estimation. We also consider
extensions to heteroskedastic precision introduced in the literature
(Lakshminarayanan, Bouchard, and Archambeau, 2011). We show that this tends
result in overfitting for deterministic approximation algorithms (ex:
Variational inference) when the observed entries in the user / item matrix are
distributed in an non-uniform manner. In light of this, we propose a truncated
precision model. Our experimental results suggest that this model tends to
delay overfitting.
Sure Screening for Gaussian Graphical Models
Authors: Shikai Luo, Rui Song, Daniela Witten
Date: 2014-07-29 00:00:00

We propose {graphical sure screening}, or GRASS, a very simple and
computationally-efficient screening procedure for recovering the structure of a
Gaussian graphical model in the high-dimensional setting. The GRASS estimate of
the conditional dependence graph is obtained by thresholding the elements of
the sample covariance matrix. The proposed approach possesses the sure
screening property: with very high probability, the GRASS estimated edge set
contains the true edge set. Furthermore, with high probability, the size of the
estimated edge set is controlled. We provide a choice of threshold for GRASS
that can control the expected false positive rate. We illustrate the
performance of GRASS in a simulation study and on a gene expression data set,
and show that in practice it performs quite competitively with more complex and
computationally-demanding techniques for graph estimation.
Understanding the Complexity of Lifted Inference and Asymmetric Weighted Model Counting
Authors: Eric Gribkoff, Guy Van Den Broeck, Dan Suciu
Date: 2014-05-13 00:00:00
Tasks: Relational Reasoning, Sentence

In this paper we study lifted inference for the Weighted First-Order Model
Counting problem (WFOMC), which counts the assignments that satisfy a given
sentence in first-order logic (FOL); it has applications in Statistical
Relational Learning (SRL) and Probabilistic Databases (PDB). We present several
results. First, we describe a lifted inference algorithm that generalizes prior
approaches in SRL and PDB. Second, we provide a novel dichotomy result for a
non-trivial fragment of FO CNF sentences, showing that for each sentence the
WFOMC problem is either in PTIME or #P-hard in the size of the input domain; we
prove that, in the first case our algorithm solves the WFOMC problem in PTIME,
and in the second case it fails. Third, we present several properties of the
algorithm. Finally, we discuss limitations of lifted inference for symmetric
probabilistic databases (where the weights of ground literals depend only on
the relation name, and not on the constants of the domain), and prove the
impossibility of a dichotomy result for the complexity of probabilistic
inference for the entire language FOL.
The Power of Online Learning in Stochastic Network Optimization
Authors: Longbo Huang, Xin Liu, Xiaohong Hao
Date: 2014-04-06 00:00:00

In this paper, we investigate the power of online learning in stochastic
network optimization with unknown system statistics {\it a priori}. We are
interested in understanding how information and learning can be efficiently
incorporated into system control techniques, and what are the fundamental
benefits of doing so. We propose two \emph{Online Learning-Aided Control}
techniques, $\mathtt{OLAC}$ and $\mathtt{OLAC2}$, that explicitly utilize the
past system information in current system control via a learning procedure
called \emph{dual learning}. We prove strong performance guarantees of the
proposed algorithms: $\mathtt{OLAC}$ and $\mathtt{OLAC2}$ achieve the
near-optimal $[O(\epsilon), O([\log(1/\epsilon)]^2)]$ utility-delay tradeoff
and $\mathtt{OLAC2}$ possesses an $O(\epsilon^{-2/3})$ convergence time.
$\mathtt{OLAC}$ and $\mathtt{OLAC2}$ are probably the first algorithms that
simultaneously possess explicit near-optimal delay guarantee and sub-linear
convergence time. Simulation results also confirm the superior performance of
the proposed algorithms in practice. To the best of our knowledge, our attempt
is the first to explicitly incorporate online learning into stochastic network
optimization and to demonstrate its power in both theory and practice.
A Hash-based Co-Clustering Algorithm for Categorical Data
Authors: Fabricio Olivetti de França
Date: 2014-07-29 00:00:00
Tasks: Clustering, feature selection

Many real-life data are described by categorical attributes without a
pre-classification. A common data mining method used to extract information
from this type of data is clustering. This method group together the samples
from the data that are more similar than all other samples. But, categorical
data pose a challenge when extracting information because: the calculation of
two objects similarity is usually done by measuring the number of common
features, but ignore a possible importance weighting; if the data may be
divided differently according to different subsets of the features, the
algorithm may find clusters with different meanings from each other,
difficulting the post analysis. Data Co-Clustering of categorical data is the
technique that tries to find subsets of samples that share a subset of features
in common. By doing so, not only a sample may belong to more than one cluster
but, the feature selection of each cluster describe its own characteristics. In
this paper a novel Co-Clustering technique for categorical data is proposed by
using Locality Sensitive Hashing technique in order to preprocess a list of
Co-Clusters seeds based on a previous research. Results indicate this technique
is capable of finding high quality Co-Clusters in many different categorical
data sets and scales linearly with the data set size.
A CUDA-Based Real Parameter Optimization Benchmark
Authors: Ke Ding, Ying Tan
Date: 2014-07-29 00:00:00
Tasks: Benchmarking, CPU

Benchmarking is key for developing and comparing optimization algorithms. In
this paper, a CUDA-based real parameter optimization benchmark (cuROB) is
introduced. Test functions of diverse properties are included within cuROB and
implemented efficiently with CUDA. Speedup of one order of magnitude can be
achieved in comparison with CPU-based benchmark of CEC'14.
A Latent Space Analysis of Editor Lifecycles in Wikipedia
Authors: Xiangju Qin, Derek Greene, Pádraig Cunningham
Date: 2014-07-29 00:00:00

Collaborations such as Wikipedia are a key part of the value of the modern
Internet. At the same time there is concern that these collaborations are
threatened by high levels of member turnover. In this paper we borrow ideas
from topic analysis to editor activity on Wikipedia over time into a latent
space that offers an insight into the evolving patterns of editor behavior.
This latent space representation reveals a number of different categories of
editor (e.g. content experts, social networkers) and we show that it does
provide a signal that predicts an editor's departure from the community. We
also show that long term editors gradually diversify their participation by
shifting edit preference from one or two namespaces to multiple namespaces and
experience relatively soft evolution in their editor profiles, while short term
editors generally distribute their contribution randomly among the namespaces
and experience considerably fluctuated evolution in their editor profiles.
NMF with Sparse Regularizations in Transformed Domains
Authors: Jérémy Rapin, Jérôme Bobin, Anthony Larue, Jean-Luc Starck
Date: 2014-07-29 00:00:00
Tasks: blind source separation, Retrieval

Non-negative blind source separation (non-negative BSS), which is also
referred to as non-negative matrix factorization (NMF), is a very active field
in domains as different as astrophysics, audio processing or biomedical signal
processing. In this context, the efficient retrieval of the sources requires
the use of signal priors such as sparsity. If NMF has now been well studied
with sparse constraints in the direct domain, only very few algorithms can
encompass non-negativity together with sparsity in a transformed domain since
simultaneously dealing with two priors in two different domains is challenging.
In this article, we show how a sparse NMF algorithm coined non-negative
generalized morphological component analysis (nGMCA) can be extended to impose
non-negativity in the direct domain along with sparsity in a transformed
domain, with both analysis and synthesis formulations. To our knowledge, this
work presents the first comparison of analysis and synthesis priors ---as well
as their reweighted versions--- in the context of blind source separation.
Comparisons with state-of-the-art NMF algorithms on realistic data show the
efficiency as well as the robustness of the proposed algorithms.
Hyperspectral Imaging and Analysis for Sparse Reconstruction and Recognition
Authors: Zohaib Khan
Date: 2014-07-29 00:00:00
Tasks: Dimensionality Reduction, Hyperspectral image analysis

This thesis proposes spatio-spectral techniques for hyperspectral image
analysis. Adaptive spatio-spectral support and variable exposure hyperspectral
imaging is demonstrated to improve spectral reflectance recovery from
hyperspectral images. Novel spectral dimensionality reduction techniques have
been proposed from the perspective of spectral only and spatio-spectral
information preservation. It was found that the joint sparse and joint group
sparse hyperspectral image models achieve lower reconstruction error and higher
recognition accuracy using only a small subset of bands. Hyperspectral image
databases have been developed and made publicly available for further research
in compressed hyperspectral imaging, forensic document analysis and spectral
reflectance recovery.
Chasing Ghosts: Competing with Stateful Policies
Authors: Uriel Feige, Tomer Koren, Moshe Tennenholtz
Date: 2014-07-29 00:00:00
Tasks: Attribute, Decision Making, LEMMA, Sequential Decision Making

We consider sequential decision making in a setting where regret is measured
with respect to a set of stateful reference policies, and feedback is limited
to observing the rewards of the actions performed (the so called "bandit"
setting). If either the reference policies are stateless rather than stateful,
or the feedback includes the rewards of all actions (the so called "expert"
setting), previous work shows that the optimal regret grows like
$\Theta(\sqrt{T})$ in terms of the number of decision rounds $T$.
  The difficulty in our setting is that the decision maker unavoidably loses
track of the internal states of the reference policies, and thus cannot
reliably attribute rewards observed in a certain round to any of the reference
policies. In fact, in this setting it is impossible for the algorithm to
estimate which policy gives the highest (or even approximately highest) total
reward. Nevertheless, we design an algorithm that achieves expected regret that
is sublinear in $T$, of the form $O( T/\log^{1/4}{T})$. Our algorithm is based
on a certain local repetition lemma that may be of independent interest. We
also show that no algorithm can guarantee expected regret better than $O(
T/\log^{3/2} T)$.
A Survey on Two Dimensional Cellular Automata and Its Application in Image Processing
Authors: Deepak Ranjan Nayak, Prashanta Kumar Patra, Amitav Mahapatra
Date: 2014-07-29 00:00:00
Tasks: Edge Detection, Survey, Translation

Parallel algorithms for solving any image processing task is a highly
demanded approach in the modern world. Cellular Automata (CA) are the most
common and simple models of parallel computation. So, CA has been successfully
used in the domain of image processing for the last couple of years. This paper
provides a survey of available literatures of some methodologies employed by
different researchers to utilize the cellular automata for solving some
important problems of image processing. The survey includes some important
image processing tasks such as rotation, zooming, translation, segmentation,
edge detection, compression and noise reduction of images. Finally, the
experimental results of some methodologies are presented.
Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
Authors: Alekh Agarwal, Animashree Anandkumar, Prateek Jain, Praneeth Netrapalli
Date: 2013-10-30 00:00:00

We consider the problem of sparse coding, where each sample consists of a
sparse linear combination of a set of dictionary atoms, and the task is to
learn both the dictionary elements and the mixing coefficients. Alternating
minimization is a popular heuristic for sparse coding, where the dictionary and
the coefficients are estimated in alternate steps, keeping the other fixed.
Typically, the coefficients are estimated via $\ell_1$ minimization, keeping
the dictionary fixed, and the dictionary is estimated through least squares,
keeping the coefficients fixed. In this paper, we establish local linear
convergence for this variant of alternating minimization and establish that the
basin of attraction for the global optimum (corresponding to the true
dictionary and the coefficients) is $\order{1/s^2}$, where $s$ is the sparsity
level in each sample and the dictionary satisfies RIP. Combined with the recent
results of approximate dictionary estimation, this yields provable guarantees
for exact recovery of both the dictionary elements and the coefficients, when
the dictionary elements are incoherent.
Dynamic Feature Scaling for Online Learning of Binary Classifiers
Authors: Danushka Bollegala
Date: 2014-07-28 00:00:00
Tasks: Binary Classification, General Classification

Scaling feature values is an important step in numerous machine learning
tasks. Different features can have different value ranges and some form of a
feature scaling is often required in order to learn an accurate classifier.
However, feature scaling is conducted as a preprocessing task prior to
learning. This is problematic in an online setting because of two reasons.
First, it might not be possible to accurately determine the value range of a
feature at the initial stages of learning when we have observed only a few
number of training instances. Second, the distribution of data can change over
the time, which render obsolete any feature scaling that we perform in a
pre-processing step. We propose a simple but an effective method to dynamically
scale features at train time, thereby quickly adapting to any changes in the
data stream. We compare the proposed dynamic feature scaling method against
more complex methods for estimating scaling parameters using several benchmark
datasets for binary classification. Our proposed feature scaling method
consistently outperforms more complex methods on all of the benchmark datasets
and improves classification accuracy of a state-of-the-art online binary
classifier algorithm.
Dependence versus Conditional Dependence in Local Causal Discovery from Gene Expression Data
Authors: Eric V. Strobl, Shyam Visweswaran
Date: 2014-07-28 00:00:00
Tasks: Causal Discovery

Motivation: Algorithms that discover variables which are causally related to
a target may inform the design of experiments. With observational gene
expression data, many methods discover causal variables by measuring each
variable's degree of statistical dependence with the target using dependence
measures (DMs). However, other methods measure each variable's ability to
explain the statistical dependence between the target and the remaining
variables in the data using conditional dependence measures (CDMs), since this
strategy is guaranteed to find the target's direct causes, direct effects, and
direct causes of the direct effects in the infinite sample limit. In this
paper, we design a new algorithm in order to systematically compare the
relative abilities of DMs and CDMs in discovering causal variables from gene
expression data.
  Results: The proposed algorithm using a CDM is sample efficient, since it
consistently outperforms other state-of-the-art local causal discovery
algorithms when samples sizes are small. However, the proposed algorithm using
a CDM outperforms the proposed algorithm using a DM only when sample sizes are
above several hundred. These results suggest that accurate causal discovery
from gene expression data using current CDM-based algorithms requires datasets
with at least several hundred samples.
  Availability: The proposed algorithm is freely available at
https://github.com/ericstrobl/DvCD.