pwc-archive/papers-with-abstracts huggingface dataset:
Optimization of anemia treatment in hemodialysis patients via reinforcement learning
Authors: Pablo Escandell-Montero, Milena Chermisi, José M. Martínez-Martínez, Juan Gómez-Sanchis, Carlo Barbieri, Emilio Soria-Olivas, Flavio Mari, Joan Vila-Francés, Andrea Stopper, Emanuele Gatti, José D. Martín-Guerrero
Date: 2015-09-14 00:00:00
Tasks: Decision Making, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Sequential Decision Making

Objective: Anemia is a frequent comorbidity in hemodialysis patients that can
be successfully treated by administering erythropoiesis-stimulating agents
(ESAs). ESAs dosing is currently based on clinical protocols that often do not
account for the high inter- and intra-individual variability in the patient's
response. As a result, the hemoglobin level of some patients oscillates around
the target range, which is associated with multiple risks and side-effects.
This work proposes a methodology based on reinforcement learning (RL) to
optimize ESA therapy.
  Methods: RL is a data-driven approach for solving sequential decision-making
problems that are formulated as Markov decision processes (MDPs). Computing
optimal drug administration strategies for chronic diseases is a sequential
decision-making problem in which the goal is to find the best sequence of drug
doses. MDPs are particularly suitable for modeling these problems due to their
ability to capture the uncertainty associated with the outcome of the treatment
and the stochastic nature of the underlying process. The RL algorithm employed
in the proposed methodology is fitted Q iteration, which stands out for its
ability to make an efficient use of data.
  Results: The experiments reported here are based on a computational model
that describes the effect of ESAs on the hemoglobin level. The performance of
the proposed method is evaluated and compared with the well-known Q-learning
algorithm and with a standard protocol. Simulation results show that the
performance of Q-learning is substantially lower than FQI and the protocol.
  Conclusion: Although prospective validation is required, promising results
demonstrate the potential of RL to become an alternative to current protocols.
Natural scene statistics mediate the perception of image complexity
Authors: Nicolas Gauvrit, Fernando Soler-Toscano, Hector Zenil
Date: 2015-09-14 00:00:00

Humans are sensitive to complexity and regularity in patterns. The subjective
perception of pattern complexity is correlated to algorithmic
(Kolmogorov-Chaitin) complexity as defined in computer science, but also to the
frequency of naturally occurring patterns. However, the possible mediational
role of natural frequencies in the perception of algorithmic complexity remains
unclear. Here we reanalyze Hsu et al. (2010) through a mediational analysis,
and complement their results in a new experiment. We conclude that human
perception of complexity seems partly shaped by natural scenes statistics,
thereby establishing a link between the perception of complexity and the effect
of natural scene statistics.
Learning to Divide and Conquer for Online Multi-Target Tracking
Authors: Francesco Solera, Simone Calderara, Rita Cucchiara
Date: 2015-09-14 00:00:00
Proceeding: ICCV 2015 12

Online Multiple Target Tracking (MTT) is often addressed within the
tracking-by-detection paradigm. Detections are previously extracted
independently in each frame and then objects trajectories are built by
maximizing specifically designed coherence functions. Nevertheless, ambiguities
arise in presence of occlusions or detection errors. In this paper we claim
that the ambiguities in tracking could be solved by a selective use of the
features, by working with more reliable features if possible and exploiting a
deeper representation of the target only if necessary. To this end, we propose
an online divide and conquer tracker for static camera scenes, which partitions
the assignment problem in local subproblems and solves them by selectively
choosing and combining the best features. The complete framework is cast as a
structural learning task that unifies these phases and learns tracker
parameters from examples. Experiments on two different datasets highlights a
significant improvement of tracking performances (MOTA +10%) over the state of
the art.
Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions
Authors: Yoshinobu Kawahara, Yutaro Yamaguchi
Date: 2015-09-14 00:00:00
Tasks: Sparse Learning

The proximal problem for structured penalties obtained via convex relaxations
of submodular functions is known to be equivalent to minimizing separable
convex functions over the corresponding submodular polyhedra. In this paper, we
reveal a comprehensive class of structured penalties for which penalties this
problem can be solved via an efficiently solvable class of parametric maxflow
optimization. We then show that the parametric maxflow algorithm proposed by
Gallo et al. and its variants, which runs, in the worst-case, at the cost of
only a constant factor of a single computation of the corresponding maxflow
optimization, can be adapted to solve the proximal problems for those
penalties. Several existing structured penalties satisfy these conditions;
thus, regularized learning with these penalties is solvable quickly using the
parametric maxflow algorithm. We also investigate the empirical runtime
performance of the proposed framework.
Geometry and dimensionality reduction of feature spaces in primary visual cortex
Authors: Davide Barbieri
Date: 2015-09-14 00:00:00
Tasks: Dimensionality Reduction

Some geometric properties of the wavelet analysis performed by visual neurons
are discussed and compared with experimental data. In particular, several
relationships between the cortical morphologies and the parametric dependencies
of extracted features are formalized and considered from a harmonic analysis
point of view.
Learning Social Relation Traits from Face Images
Authors: Zhanpeng Zhang, Ping Luo, Chen Change Loy, Xiaoou Tang
Date: 2015-09-14 00:00:00
Proceeding: ICCV 2015 12
Tasks: Attribute, Relation, Relation Prediction

Social relation defines the association, e.g, warm, friendliness, and
dominance, between two or more people. Motivated by psychological studies, we
investigate if such fine-grained and high-level relation traits can be
characterised and quantified from face images in the wild. To address this
challenging problem we propose a deep model that learns a rich face
representation to capture gender, expression, head pose, and age-related
attributes, and then performs pairwise-face reasoning for relation prediction.
To learn from heterogeneous attribute sources, we formulate a new network
architecture with a bridging layer to leverage the inherent correspondences
among these datasets. It can also cope with missing target attribute labels.
Extensive experiments show that our approach is effective for fine-grained
social relation learning in images and videos.
Markov Boundary Discovery with Ridge Regularized Linear Models
Authors: Eric V. Strobl, Shyam Visweswaran
Date: 2015-09-14 00:00:00
Tasks: Causal Inference, Dimensionality Reduction, Two-sample testing

Ridge regularized linear models (RRLMs), such as ridge regression and the
SVM, are a popular group of methods that are used in conjunction with
coefficient hypothesis testing to discover explanatory variables with a
significant multivariate association to a response. However, many investigators
are reluctant to draw causal interpretations of the selected variables due to
the incomplete knowledge of the capabilities of RRLMs in causal inference.
Under reasonable assumptions, we show that a modified form of RRLMs can get
very close to identifying a subset of the Markov boundary by providing a
worst-case bound on the space of possible solutions. The results hold for any
convex loss, even when the underlying functional relationship is nonlinear, and
the solution is not unique. Our approach combines ideas in Markov boundary and
sufficient dimension reduction theory. Experimental results show that the
modified RRLMs are competitive against state-of-the-art algorithms in
discovering part of the Markov boundary from gene expression data.
A new estimate of mutual information based measure of dependence between two variables: properties and fast implementation
Authors: Namita Jain, C. A. Murthy
Date: 2014-10-28 00:00:00
Tasks: Maximum Separation

This article proposes a new method to estimate an existing mutual information
based dependence measure using histogram density estimates. Finding a suitable
bin length for histogram is an open problem. We propose a new way of computing
the bin length for histogram using a function of maximum separation between
points. The chosen bin length leads to consistent density estimates for
histogram method. The values of density thus obtained are used to calculate an
estimate of an existing dependence measure. The proposed estimate is named as
Mutual Information Based Dependence Index (MIDI). Some important properties of
MIDI have also been stated. The performance of the proposed method has been
compared to generally accepted measures like Distance Correlation (dcor),
Maximal Information Coefficient (MINE) in terms of accuracy and computational
complexity with the help of several artificial data sets with different amounts
of noise. The proposed method is able to detect many types of relationships
between variables, without making any assumption about the functional form of
the relationship. The power statistics of proposed method illustrate their
effectiveness in detecting non linear relationship. Thus, it is able to achieve
generality without a high rate of false positive cases. MIDI is found to work
better on a real life data set than competing methods. The proposed method is
found to overcome some of the limitations which occur with dcor and MINE.
Computationally, MIDI is found to be better than dcor and MINE, in terms of
time and memory, making it suitable for large data sets.
A More Powerful Two-Sample Test in High Dimensions using Random Projection
Authors: Miles E. Lopes, Laurent J. Jacob, Martin J. Wainwright
Date: 2011-08-11 00:00:00
Proceeding: NeurIPS 2011 12
Tasks: Two-sample testing

We consider the hypothesis testing problem of detecting a shift between the
means of two multivariate normal distributions in the high-dimensional setting,
allowing for the data dimension p to exceed the sample size n. Specifically, we
propose a new test statistic for the two-sample test of means that integrates a
random projection with the classical Hotelling T^2 statistic. Working under a
high-dimensional framework with (p,n) tending to infinity, we first derive an
asymptotic power function for our test, and then provide sufficient conditions
for it to achieve greater power than other state-of-the-art tests. Using ROC
curves generated from synthetic data, we demonstrate superior performance
against competing tests in the parameter regimes anticipated by our theoretical
results. Lastly, we illustrate an advantage of our procedure's false positive
rate with comparisons on high-dimensional gene expression data involving the
discrimination of different types of cancer.
On Binary Classification with Single-Layer Convolutional Neural Networks
Authors: Soroush Mehri
Date: 2015-09-13 00:00:00
Tasks: Binary Classification, Classification, General Classification, Object Recognition

Convolutional neural networks are becoming standard tools for solving object
recognition and visual tasks. However, most of the design and implementation of
these complex models are based on trail-and-error. In this report, the main
focus is to consider some of the important factors in designing convolutional
networks to perform better. Specifically, classification with wide single-layer
networks with large kernels as a general framework is considered. Particularly,
we will show that pre-training using unsupervised schemes is vital, reasonable
regularization is beneficial and applying of strong regularizers like dropout
could be devastating. Pool size is also could be as important as learning
procedure itself. In addition, it has been presented that using such a simple
and relatively fast model for classifying cats and dogs, performance is close
to state-of-the-art achievable by a combination of SVM models on color and
texture features.
The USFD Spoken Language Translation System for IWSLT 2014
Authors: Raymond W. M. Ng, Mortaza Doulaty, Rama Doddipatla, Wilker Aziz, Kashif Shah, Oscar Saz, Madina Hasan, Ghada Alharbi, Lucia Specia, Thomas Hain
Date: 2015-09-13 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Machine Translation, speech-recognition, Speech Recognition, Speech-to-Text, Speech-to-Text Translation, Translation

The University of Sheffield (USFD) participated in the International Workshop
for Spoken Language Translation (IWSLT) in 2014. In this paper, we will
introduce the USFD SLT system for IWSLT. Automatic speech recognition (ASR) is
achieved by two multi-pass deep neural network systems with adaptation and
rescoring techniques. Machine translation (MT) is achieved by a phrase-based
system. The USFD primary system incorporates state-of-the-art ASR and MT
techniques and gives a BLEU score of 23.45 and 14.75 on the English-to-French
and English-to-German speech-to-text translation task with the IWSLT 2014 data.
The USFD contrastive systems explore the integration of ASR and MT by using a
quality estimation system to rescore the ASR outputs, optimising towards better
translation. This gives a further 0.54 and 0.26 BLEU improvement respectively
on the IWSLT 2012 and 2014 evaluation data.
Vectors of Locally Aggregated Centers for Compact Video Representation
Authors: Alhabib Abbas, Nikos Deligiannis, Yiannis Andreopoulos
Date: 2015-09-13 00:00:00
Tasks: Clustering, Video Description

We propose a novel vector aggregation technique for compact video
representation, with application in accurate similarity detection within large
video datasets. The current state-of-the-art in visual search is formed by the
vector of locally aggregated descriptors (VLAD) of Jegou et. al. VLAD generates
compact video representations based on scale-invariant feature transform (SIFT)
vectors (extracted per frame) and local feature centers computed over a
training set. With the aim to increase robustness to visual distortions, we
propose a new approach that operates at a coarser level in the feature
representation. We create vectors of locally aggregated centers (VLAC) by first
clustering SIFT features to obtain local feature centers (LFCs) and then
encoding the latter with respect to given centers of local feature centers
(CLFCs), extracted from a training set. The sum-of-differences between the LFCs
and the CLFCs are aggregated to generate an extremely-compact video description
used for accurate video segment similarity detection. Experimentation using a
video dataset, comprising more than 1000 minutes of content from the Open Video
Project, shows that VLAC obtains substantial gains in terms of mean Average
Precision (mAP) against VLAD and the hyper-pooling method of Douze et. al.,
under the same compaction factor and the same set of distortions.
Statistical Analysis of Loopy Belief Propagation in Random Fields
Authors: Muneki Yasuda, Shun Kataoka, Kazuyuki Tanaka
Date: 2015-03-16 00:00:00
Tasks: Image Restoration

Loopy belief propagation (LBP), which is equivalent to the Bethe
approximation in statistical mechanics, is a message-passing-type inference
method that is widely used to analyze systems based on Markov random fields
(MRFs). In this paper, we propose a message-passing-type method to analytically
evaluate the quenched average of LBP in random fields by using the replica
cluster variation method. The proposed analytical method is applicable to
general pair-wise MRFs with random fields whose distributions differ from each
other and can give the quenched averages of the Bethe free energies over random
fields, which are consistent with numerical results. The order of its
computational cost is equivalent to that of standard LBP. In the latter part of
this paper, we describe the application of the proposed method to Bayesian
image restoration, in which we observed that our theoretical results are in
good agreement with the numerical results for natural images.
Sample Complexity Bounds on Differentially Private Learning via Communication Complexity
Authors: Vitaly Feldman, David Xiao
Date: 2014-02-25 00:00:00
Tasks: PAC learning

In this work we analyze the sample complexity of classification by
differentially private algorithms. Differential privacy is a strong and
well-studied notion of privacy introduced by Dwork et al. (2006) that ensures
that the output of an algorithm leaks little information about the data point
provided by any of the participating individuals. Sample complexity of private
PAC and agnostic learning was studied in a number of prior works starting with
(Kasiviswanathan et al., 2008) but a number of basic questions still remain
open, most notably whether learning with privacy requires more samples than
learning without privacy.
  We show that the sample complexity of learning with (pure) differential
privacy can be arbitrarily higher than the sample complexity of learning
without the privacy constraint or the sample complexity of learning with
approximate differential privacy. Our second contribution and the main tool is
an equivalence between the sample complexity of (pure) differentially private
learning of a concept class $C$ (or $SCDP(C)$) and the randomized one-way
communication complexity of the evaluation problem for concepts from $C$. Using
this equivalence we prove the following bounds:
  1. $SCDP(C) = \Omega(LDim(C))$, where $LDim(C)$ is the Littlestone's (1987)
dimension characterizing the number of mistakes in the online-mistake-bound
learning model. Known bounds on $LDim(C)$ then imply that $SCDP(C)$ can be much
higher than the VC-dimension of $C$.
  2. For any $t$, there exists a class $C$ such that $LDim(C)=2$ but $SCDP(C)
\geq t$.
  3. For any $t$, there exists a class $C$ such that the sample complexity of
(pure) $\alpha$-differentially private PAC learning is $\Omega(t/\alpha)$ but
the sample complexity of the relaxed $(\alpha,\beta)$-differentially private
PAC learning is $O(\log(1/\beta)/\alpha)$. This resolves an open problem of
Beimel et al. (2013b).
A Dual Fast and Slow Feature Interaction in Biologically Inspired Visual Recognition of Human Action
Authors: Bardia Yousefi, C. K. Loo
Date: 2015-09-09 00:00:00
Tasks: Form, Optical Flow Estimation

Computational neuroscience studies that have examined human visual system
through functional magnetic resonance imaging (fMRI) have identified a model
where the mammalian brain pursues two distinct pathways (for recognition of
biological movement tasks). In the brain, dorsal stream analyzes the
information of motion (optical flow), which is the fast features, and ventral
stream (form pathway) analyzes form information (through active basis model
based incremental slow feature analysis ) as slow features. The proposed
approach suggests the motion perception of the human visual system composes of
fast and slow feature interactions that identifies biological movements. Form
features in the visual system biologically follows the application of active
basis model with incremental slow feature analysis for the extraction of the
slowest form features of human objects movements in the ventral stream.
Applying incremental slow feature analysis provides an opportunity to use the
action prototypes. To extract the slowest features episodic observation is
required but the fast features updates the processing of motion information in
every frames. Experimental results have shown promising accuracy for the
proposed model and good performance with two datasets (KTH and Weizmann).
Compositional Dictionaries for Domain Adaptive Face Recognition
Authors: Qiang Qiu, Rama Chellappa
Date: 2013-08-01 00:00:00
Tasks: Dictionary Learning, Face Recognition

We present a dictionary learning approach to compensate for the transformation of faces due to changes in view point, illumination, resolution, etc. The key idea of our approach is to force domain-invariant sparse coding, i.e., design a consistent sparse representation of the same face in different domains. In this way, classifiers trained on the sparse codes in the source domain consisting of frontal faces for example can be applied to the target domain (consisting of faces in different poses, illumination conditions, etc) without much loss in recognition accuracy. The approach is to first learn a domain base dictionary, and then describe each domain shift (identity, pose, illumination) using a sparse representation over the base dictionary. The dictionary adapted to each domain is expressed as sparse linear combinations of the base dictionary. In the context of face recognition, with the proposed compositional dictionary approach, a face image can be decomposed into sparse representations for a given subject, pose and illumination respectively. This approach has three advantages: first, the extracted sparse representation for a subject is consistent across domains and enables pose and illumination insensitive face recognition. Second, sparse representations for pose and illumination can subsequently be used to estimate the pose and illumination condition of a face image. Finally, by composing sparse representations for subject and the different domains, we can also perform pose alignment and illumination normalization. Extensive experiments using two public face datasets are presented to demonstrate the effectiveness of our approach for face recognition.
Generalized Uniformly Optimal Methods for Nonlinear Programming
Authors: Saeed Ghadimi, Guanghui Lan, Hongchao Zhang
Date: 2015-08-29 00:00:00

In this paper, we present a generic framework to extend existing uniformly
optimal convex programming algorithms to solve more general nonlinear, possibly
nonconvex, optimization problems. The basic idea is to incorporate a local
search step (gradient descent or Quasi-Newton iteration) into these uniformly
optimal convex programming methods, and then enforce a monotone decreasing
property of the function values computed along the trajectory. Algorithms of
these types will then achieve the best known complexity for nonconvex problems,
and the optimal complexity for convex ones without requiring any problem
parameters. As a consequence, we can have a unified treatment for a general
class of nonlinear programming problems regardless of their convexity and
smoothness level. In particular, we show that the accelerated gradient and
level methods, both originally designed for solving convex optimization
problems only, can be used for solving both convex and nonconvex problems
uniformly. In a similar vein, we show that some well-studied techniques for
nonlinear programming, e.g., Quasi-Newton iteration, can be embedded into
optimal convex optimization algorithms to possibly further enhance their
numerical performance. Our theoretical and algorithmic developments are
complemented by some promising numerical results obtained for solving a few
important nonconvex and nonlinear data analysis problems in the literature.
Improving distant supervision using inference learning
Authors: Roland Roller, Eneko Agirre, Aitor Soroa, Mark Stevenson
Date: 2015-09-12 00:00:00
Proceeding: IJCNLP 2015 7
Tasks: Relation, Relation Extraction

Distant supervision is a widely applied approach to automatic training of
relation extraction systems and has the advantage that it can generate large
amounts of labelled data with minimal effort. However, this data may contain
errors and consequently systems trained using distant supervision tend not to
perform as well as those based on manually labelled data. This work proposes a
novel method for detecting potential false negative training examples using a
knowledge inference method. Results show that our approach improves the
performance of relation extraction systems trained using distantly supervised
data.