pwc-archive/papers-with-abstracts huggingface dataset:
Transfer Learning for Content-Based Recommender Systems using Tree Matching
Authors: Naseem Biadsy, Lior Rokach, Armin Shmilovici
Date: 2013-05-15 00:00:00
Tasks: Recommendation Systems, Transfer Learning

In this paper we present a new approach to content-based transfer learning
for solving the data sparsity problem in cases when the users' preferences in
the target domain are either scarce or unavailable, but the necessary
information on the preferences exists in another domain. We show that training
a system to use such information across domains can produce better performance.
Specifically, we represent users' behavior patterns based on topological graph
structures. Each behavior pattern represents the behavior of a set of users,
when the users' behavior is defined as the items they rated and the items'
rating values. In the next step we find a correlation between behavior patterns
in the source domain and behavior patterns in the target domain. This mapping
is considered a bridge between the two domains. Based on the correlation and
content-attributes of the items, we train a machine learning model to predict
users' ratings in the target domain. When we compare our approach to the
popularity approach and KNN-cross-domain on a real world dataset, the results
show that on an average of 83$%$ of the cases our approach outperforms both
methods.
Color image denoising by chromatic edges based vector valued diffusion
Authors: V. B. Surya Prasath, Juan C. Moreno, K. Palaniappan
Date: 2013-04-20 00:00:00
Tasks: Color Image Denoising, Denoising, Image Denoising

In this letter we propose to denoise digital color images via an improved
geometric diffusion scheme. By introducing edges detected from all three color
channels into the diffusion the proposed scheme avoids color smearing
artifacts. Vector valued diffusion is used to control the smoothing and the
geometry of color images are taken into consideration. Color edge strength
function computed from different planes is introduced and it stops the
diffusion spread across chromatic edges. Experimental results indicate that the
scheme achieves good denoising with edge preservation when compared to other
related schemes.
Online Learning in a Contract Selection Problem
Authors: Cem Tekin, Mingyan Liu
Date: 2013-05-15 00:00:00
Tasks: Recommendation Systems

In an online contract selection problem there is a seller which offers a set
of contracts to sequentially arriving buyers whose types are drawn from an
unknown distribution. If there exists a profitable contract for the buyer in
the offered set, i.e., a contract with payoff higher than the payoff of not
accepting any contracts, the buyer chooses the contract that maximizes its
payoff. In this paper we consider the online contract selection problem to
maximize the sellers profit. Assuming that a structural property called ordered
preferences holds for the buyer's payoff function, we propose online learning
algorithms that have sub-linear regret with respect to the best set of
contracts given the distribution over the buyer's type. This problem has many
applications including spectrum contracts, wireless service provider data plans
and recommendation systems.
A Mining-Based Compression Approach for Constraint Satisfaction Problems
Authors: Said Jabbour, Lakhdar Sais, Yakoub Salhi
Date: 2013-05-14 00:00:00

In this paper, we propose an extension of our Mining for SAT framework to
Constraint satisfaction Problem (CSP). We consider n-ary extensional
constraints (table constraints). Our approach aims to reduce the size of the
CSP by exploiting the structure of the constraints graph and of its associated
microstructure. More precisely, we apply itemset mining techniques to search
for closed frequent itemsets on these two representation. Using Tseitin
extension, we rewrite the whole CSP to another compressed CSP equivalent with
respect to satisfiability. Our approach contrast with previous proposed
approach by Katsirelos and Walsh, as we do not change the structure of the
constraints.
Feature Multi-Selection among Subjective Features
Authors: Sivan Sabato, Adam Kalai
Date: 2013-02-18 00:00:00
Tasks: regression

When dealing with subjective, noisy, or otherwise nebulous features, the
"wisdom of crowds" suggests that one may benefit from multiple judgments of the
same feature on the same object. We give theoretically-motivated `feature
multi-selection' algorithms that choose, among a large set of candidate
features, not only which features to judge but how many times to judge each
one. We demonstrate the effectiveness of this approach for linear regression on
a crowdsourced learning task of predicting people's height and weight from
photos, using features such as 'gender' and 'estimated weight' as well as
culturally fraught ones such as 'attractive'.
Efficient Density Estimation via Piecewise Polynomial Approximation
Authors: Siu-On Chan, Ilias Diakonikolas, Rocco A. Servedio, Xiaorui Sun
Date: 2013-05-14 00:00:00
Tasks: Density Estimation

We give a highly efficient "semi-agnostic" algorithm for learning univariate
probability distributions that are well approximated by piecewise polynomial
density functions. Let $p$ be an arbitrary distribution over an interval $I$
which is $\tau$-close (in total variation distance) to an unknown probability
distribution $q$ that is defined by an unknown partition of $I$ into $t$
intervals and $t$ unknown degree-$d$ polynomials specifying $q$ over each of
the intervals. We give an algorithm that draws $\tilde{O}(t\new{(d+1)}/\eps^2)$
samples from $p$, runs in time $\poly(t,d,1/\eps)$, and with high probability
outputs a piecewise polynomial hypothesis distribution $h$ that is
$(O(\tau)+\eps)$-close (in total variation distance) to $p$. This sample
complexity is essentially optimal; we show that even for $\tau=0$, any
algorithm that learns an unknown $t$-piecewise degree-$d$ probability
distribution over $I$ to accuracy $\eps$ must use $\Omega({\frac {t(d+1)}
{\poly(1 + \log(d+1))}} \cdot {\frac 1 {\eps^2}})$ samples from the
distribution, regardless of its running time. Our algorithm combines tools from
approximation theory, uniform convergence, linear programming, and dynamic
programming.
  We apply this general algorithm to obtain a wide range of results for many
natural problems in density estimation over both continuous and discrete
domains. These include state-of-the-art results for learning mixtures of
log-concave distributions; mixtures of $t$-modal distributions; mixtures of
Monotone Hazard Rate distributions; mixtures of Poisson Binomial Distributions;
mixtures of Gaussians; and mixtures of $k$-monotone densities. Our general
technique yields computationally efficient algorithms for all these problems,
in many cases with provably optimal sample complexities (up to logarithmic
factors) in all parameters.
A Bag of Words Approach for Semantic Segmentation of Monitored Scenes
Authors: Wassim Bouachir, Atousa Torabi, Guillaume-Alexandre Bilodeau, Pascal Blais
Date: 2013-05-14 00:00:00
Tasks: Segmentation, Semantic Segmentation

This paper proposes a semantic segmentation method for outdoor scenes
captured by a surveillance camera. Our algorithm classifies each perceptually
homogenous region as one of the predefined classes learned from a collection of
manually labelled images. The proposed approach combines two different types of
information. First, color segmentation is performed to divide the scene into
perceptually similar regions. Then, the second step is based on SIFT keypoints
and uses the bag of words representation of the regions for the classification.
The prediction is done using a Na\"ive Bayesian Network as a generative
classifier. Compared to existing techniques, our method provides more compact
representations of scene contents and the segmentation result is more
consistent with human perception due to the combination of the color
information with the image keypoints. The experiments conducted on a publicly
available data set demonstrate the validity of the proposed method.
Autonomous Reinforcement of Behavioral Sequences in Neural Dynamics
Authors: Sohrob Kazerounian, Matthew Luciw, Mathis Richter, Yulia Sandamirskaya
Date: 2012-10-12 00:00:00
Tasks: General Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We introduce a dynamic neural algorithm called Dynamic Neural (DN)
SARSA(\lambda) for learning a behavioral sequence from delayed reward.
DN-SARSA(\lambda) combines Dynamic Field Theory models of behavioral sequence
representation, classical reinforcement learning, and a computational
neuroscience model of working memory, called Item and Order working memory,
which serves as an eligibility trace. DN-SARSA(\lambda) is implemented on both
a simulated and real robot that must learn a specific rewarding sequence of
elementary behaviors from exploration. Results show DN-SARSA(\lambda) performs
on the level of the discrete SARSA(\lambda), validating the feasibility of
general reinforcement learning without compromising neural dynamics.
Qualitative detection of oil adulteration with machine learning approaches
Authors: Xiao-Bo Jin, Qiang Lu, Feng Wang, Quan-gong Huo
Date: 2013-05-14 00:00:00
Tasks: BIG-bench Machine Learning, Multi-Label Learning, Quantization

The study focused on the machine learning analysis approaches to identify the
adulteration of 9 kinds of edible oil qualitatively and answered the following
three questions: Is the oil sample adulterant? How does it constitute? What is
the main ingredient of the adulteration oil? After extracting the
high-performance liquid chromatography (HPLC) data on triglyceride from 370 oil
samples, we applied the adaptive boosting with multi-class Hamming loss
(AdaBoost.MH) to distinguish the oil adulteration in contrast with the support
vector machine (SVM). Further, we regarded the adulterant oil and the pure oil
samples as ones with multiple labels and with only one label, respectively.
Then multi-label AdaBoost.MH and multi-label learning vector quantization
(ML-LVQ) model were built to determine the ingredients and their relative ratio
in the adulteration oil. The experimental results on six measures show that
ML-LVQ achieves better performance than multi-label AdaBoost.MH.
Optimization with First-Order Surrogate Functions
Authors: Julien Mairal
Date: 2013-05-14 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we study optimization methods consisting of iteratively
minimizing surrogates of an objective function. By proposing several
algorithmic variants and simple convergence analyses, we make two main
contributions. First, we provide a unified viewpoint for several first-order
optimization techniques such as accelerated proximal gradient, block coordinate
descent, or Frank-Wolfe algorithms. Second, we introduce a new incremental
scheme that experimentally matches or outperforms state-of-the-art solvers for
large-scale optimization problems typically arising in machine learning.
I Wish I Didn't Say That! Analyzing and Predicting Deleted Messages in Twitter
Authors: Sasa Petrovic, Miles Osborne, Victor Lavrenko
Date: 2013-05-14 00:00:00

Twitter has become a major source of data for social media researchers. One
important aspect of Twitter not previously considered are {\em deletions} --
removal of tweets from the stream. Deletions can be due to a multitude of
reasons such as privacy concerns, rashness or attempts to undo public
statements. We show how deletions can be automatically predicted ahead of time
and analyse which tweets are likely to be deleted and how.
Multiple Change Point Estimation in Stationary Ergodic Time Series
Authors: Azadeh Khaleghi, Daniil Ryabko
Date: 2012-03-07 00:00:00
Tasks: Time Series, Time Series Analysis

Given a heterogeneous time-series sample, the objective is to find points in
time (called change points) where the probability distribution generating the
data has changed. The data are assumed to have been generated by arbitrary
unknown stationary ergodic distributions. No modelling, independence or mixing
assumptions are made. A novel, computationally efficient, nonparametric method
is proposed, and is shown to be asymptotically consistent in this general
framework. The theoretical results are complemented with experimental
evaluations.
Early Detection of Alzheimer's - A Crucial Requirement
Authors: Ijaz Bukhari
Date: 2013-05-13 00:00:00

Alzheimer's, an old age disease of people over 65 years causes problems with
memory, thinking and behavior. This disease progresses very slow and its
identification in early stages is very difficult. The symptoms of Alzheimer's
appear slowly and gradually will have worse effects. In its early stages, not
only the patients themselves but their loved ones are generally unable to
accept that the patient is suffering from disease. In this paper, we have
proposed a new algorithm to detect patients of Alzheimer's at early stages by
comparing the Magnetic Resonance Images (MRI) of the patients with normal
persons of their age. The progress of the disease can also be monitored by
periodic comparison of the previous and current MRI.
Update report: LEO-II version 1.5
Authors: Christoph Benzmüller, Nik Sultana
Date: 2013-03-15 00:00:00
Tasks: Scheduling

Recent improvements of the LEO-II theorem prover are presented. These
improvements include a revised ATP interface, new translations into first-order
logic, rule support for the axiom of choice, detection of defined equality, and
more flexible strategy scheduling.
Scalable Audience Reach Estimation in Real-time Online Advertising
Authors: Ali Jalali, Santanu Kolay, Peter Foldes, Ali Dasdan
Date: 2013-05-14 00:00:00

Online advertising has been introduced as one of the most efficient methods
of advertising throughout the recent years. Yet, advertisers are concerned
about the efficiency of their online advertising campaigns and consequently,
would like to restrict their ad impressions to certain websites and/or certain
groups of audience. These restrictions, known as targeting criteria, limit the
reachability for better performance. This trade-off between reachability and
performance illustrates a need for a forecasting system that can quickly
predict/estimate (with good accuracy) this trade-off. Designing such a system
is challenging due to (a) the huge amount of data to process, and, (b) the need
for fast and accurate estimates. In this paper, we propose a distributed fault
tolerant system that can generate such estimates fast with good accuracy. The
main idea is to keep a small representative sample in memory across multiple
machines and formulate the forecasting problem as queries against the sample.
The key challenge is to find the best strata across the past data, perform
multivariate stratified sampling while ensuring fuzzy fall-back to cover the
small minorities. Our results show a significant improvement over the uniform
and simple stratified sampling strategies which are currently widely used in
the industry.
Novel variational model for inpainting in the wavelet domain
Authors: Dai-Qiang Chen, Li-Zhi Cheng
Date: 2013-05-14 00:00:00
Tasks: Image Compression

Wavelet domain inpainting refers to the process of recovering the missing
coefficients during the image compression or transmission stage. Recently, an
efficient algorithm framework which is called Bregmanized operator splitting
(BOS) was proposed for solving the classical variational model of wavelet
inpainting. However, it is still time-consuming to some extent due to the inner
iteration. In this paper, a novel variational model is established to formulate
this reconstruction problem from the view of image decomposition. Then an
efficient iterative algorithm based on the split-Bregman method is adopted to
calculate an optimal solution, and it is also proved to be convergent. Compared
with the BOS algorithm the proposed algorithm avoids the inner iteration and
hence is more simple. Numerical experiments demonstrate that the proposed
method is very efficient and outperforms the current state-of-the-art methods,
especially in the computational time.
Real Time Bid Optimization with Smooth Budget Delivery in Online Advertising
Authors: Kuang-Chih Lee, Ali Jalali, Ali Dasdan
Date: 2013-05-14 00:00:00

Today, billions of display ad impressions are purchased on a daily basis
through a public auction hosted by real time bidding (RTB) exchanges. A
decision has to be made for advertisers to submit a bid for each selected RTB
ad request in milliseconds. Restricted by the budget, the goal is to buy a set
of ad impressions to reach as many targeted users as possible. A desired action
(conversion), advertiser specific, includes purchasing a product, filling out a
form, signing up for emails, etc. In addition, advertisers typically prefer to
spend their budget smoothly over the time in order to reach a wider range of
audience accessible throughout a day and have a sustainable impact. However,
since the conversions occur rarely and the occurrence feedback is normally
delayed, it is very challenging to achieve both budget and performance goals at
the same time. In this paper, we present an online approach to the smooth
budget delivery while optimizing for the conversion performance. Our algorithm
tries to select high quality impressions and adjust the bid price based on the
prior performance distribution in an adaptive manner by distributing the budget
optimally across time. Our experimental results from real advertising campaigns
demonstrate the effectiveness of our proposed approach.
Fast Linearized Alternating Direction Minimization Algorithm with Adaptive Parameter Selection for Multiplicative Noise Removal
Authors: Dai-Qiang Chen, Li-Zhi Cheng
Date: 2013-05-14 00:00:00

Owing to the edge preserving ability and low computational cost of the total
variation (TV), variational models with the TV regularization have been widely
investigated in the field of multiplicative noise removal. The key points of
the successful application of these models lie in: the optimal selection of the
regularization parameter which balances the data-fidelity term with the TV
regularizer; the efficient algorithm to compute the solution. In this paper, we
propose two fast algorithms based on the linearized technique, which are able
to estimate the regularization parameter and recover the image simultaneously.
In the iteration step of the proposed algorithms, the regularization parameter
is adjusted by a special discrepancy function defined for multiplicative noise.
The convergence properties of the proposed algorithms are proved under certain
conditions, and numerical experiments demonstrate that the proposed algorithms
overall outperform some state-of-the-art methods in the PSNR values and
computational time.
Estimating or Propagating Gradients Through Stochastic Neurons
Authors: Yoshua Bengio
Date: 2013-05-14 00:00:00
Tasks: Reinforcement Learning

Stochastic neurons can be useful for a number of reasons in deep learning
models, but in many cases they pose a challenging problem: how to estimate the
gradient of a loss function with respect to the input of such stochastic
neurons, i.e., can we "back-propagate" through these stochastic neurons? We
examine this question, existing approaches, and present two novel families of
solutions, applicable in different settings. In particular, it is demonstrated
that a simple biologically plausible formula gives rise to an an unbiased (but
noisy) estimator of the gradient with respect to a binary stochastic neuron
firing probability. Unlike other estimators which view the noise as a small
perturbation in order to estimate gradients by finite differences, this
estimator is unbiased even without assuming that the stochastic perturbation is
small. This estimator is also interesting because it can be applied in very
general settings which do not allow gradient back-propagation, including the
estimation of the gradient with respect to future rewards, as required in
reinforcement learning setups. We also propose an approach to approximating
this unbiased but high-variance estimator by learning to predict it using a
biased estimator. The second approach we propose assumes that an estimator of
the gradient can be back-propagated and it provides an unbiased estimator of
the gradient, but can only work with non-linearities unlike the hard threshold,
but like the rectifier, that are not flat for all of their range. This is
similar to traditional sigmoidal units but has the advantage that for many
inputs, a hard decision (e.g., a 0 output) can be produced, which would be
convenient for conditional computation and achieving sparse representations and
sparse gradients.
HRF estimation improves sensitivity of fMRI encoding and decoding models
Authors: Fabian Pedregosa, Michael Eickenberg, Bertrand Thirion, Alexandre Gramfort
Date: 2013-05-13 00:00:00
Tasks: Sensitivity

Extracting activation patterns from functional Magnetic Resonance Images
(fMRI) datasets remains challenging in rapid-event designs due to the inherent
delay of blood oxygen level-dependent (BOLD) signal. The general linear model
(GLM) allows to estimate the activation from a design matrix and a fixed
hemodynamic response function (HRF). However, the HRF is known to vary
substantially between subjects and brain regions. In this paper, we propose a
model for jointly estimating the hemodynamic response function (HRF) and the
activation patterns via a low-rank representation of task effects.This model is
based on the linearity assumption behind the GLM and can be computed using
standard gradient-based solvers. We use the activation patterns computed by our
model as input data for encoding and decoding studies and report performance
improvement in both settings.
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
Authors: David Duvenaud, James Robert Lloyd, Roger Grosse, Joshua B. Tenenbaum, Zoubin Ghahramani
Date: 2013-02-20 00:00:00
Tasks: regression, scientific discovery, Time Series, Time Series Analysis

Despite its importance, choosing the structural form of the kernel in
nonparametric regression remains a black art. We define a space of kernel
structures which are built compositionally by adding and multiplying a small
number of base kernels. We present a method for searching over this space of
structures which mirrors the scientific discovery process. The learned
structures can often decompose functions into interpretable components and
enable long-range extrapolation on time-series datasets. Our structure search
method outperforms many widely used kernels and kernel combination methods on a
variety of prediction tasks.
Hybrid fuzzy logic and pid controller based ph neutralization pilot plant
Authors: Oumair Naseer, Atif Ali Khan
Date: 2013-05-13 00:00:00
Tasks: Chemical Process

Use of Control theory within process control industries has changed rapidly
due to the increase complexity of instrumentation, real time requirements,
minimization of operating costs and highly nonlinear characteristics of
chemical process. Previously developed process control technologies which are
mostly based on a single controller are not efficient in terms of signal
transmission delays, processing power for computational needs and signal to
noise ratio. Hybrid controller with efficient system modelling is essential to
cope with the current challenges of process control in terms of control
performance. This paper presents an optimized mathematical modelling and
advance hybrid controller (Fuzzy Logic and PID) design along with practical
implementation and validation of pH neutralization pilot plant. This procedure
is particularly important for control design and automation of Physico-chemical
systems for process control industry.