pwc-archive/papers-with-abstracts huggingface dataset:
Personalized Medical Treatments Using Novel Reinforcement Learning Algorithms
Authors: Yousuf M. Soliman
Date: 2014-06-16 00:00:00
Tasks: Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In both the fields of computer science and medicine there is very strong
interest in developing personalized treatment policies for patients who have
variable responses to treatments. In particular, I aim to find an optimal
personalized treatment policy which is a non-deterministic function of the
patient specific covariate data that maximizes the expected survival time or
clinical outcome. I developed an algorithmic framework to solve multistage
decision problem with a varying number of stages that are subject to censoring
in which the "rewards" are expected survival times. In specific, I developed a
novel Q-learning algorithm that dynamically adjusts for these parameters.
Furthermore, I found finite upper bounds on the generalized error of the
treatment paths constructed by this algorithm. I have also shown that when the
optimal Q-function is an element of the approximation space, the anticipated
survival times for the treatment regime constructed by the algorithm will
converge to the optimal treatment path. I demonstrated the performance of the
proposed algorithmic framework via simulation studies and through the analysis
of chronic depression data and a hypothetical clinical trial. The censored
Q-learning algorithm I developed is more effective than the state of the art
clinical decision support systems and is able to operate in environments when
many covariate parameters may be unobtainable or censored.
Navigating Robot Swarms Using Collective Intelligence Learned from Golden Shiner Fish
Authors: Grace Gao
Date: 2014-06-30 00:00:00
Tasks: Decision Making, Navigate

Navigating networked robot swarms often requires knowing where to go, sensing
the environment, and path-planning based on the destination and barriers in the
environment. Such a process is computationally intensive. Moreover, as the
network scales up, the computational load increases quadratically, or even
exponentially. Unlike these man-made systems, most biological systems scale
linearly in complexity. Furthermore, the scale of a biological swarm can even
enable collective intelligence. One example comes from observations of golden
shiner fish. Golden shiners naturally prefer darkness and school together. Each
individual golden shiner does not know where the darkness is. Neither does it
sense the light gradients in the environment. However, by moving together as a
school, they always end up in the shady area. We apply such collective
intelligence learned from golden shiner fish to navigating robot swarms. Each
individual robot's dynamic is based on the gold shiners' movement strategy---a
random walk with its speed modulated by the light intensity and its direction
affected by its neighbors. The theoretical analysis and simulation results show
that our method 1) promises to navigate a robot swarm with little situational
knowledge, 2) simplifies control and decision-making for each individual robot,
3) requires minimal or even no information exchange within the swarm, and 4) is
highly distributed, adaptive, and robust.
Information Transfer in Swarms with Leaders
Authors: Yu Sun, Louis F. Rossi, Chien-Chung Shen, Jennifer Miller, X. Rosalind Wang, Joseph T. Lizier, Mikhail Prokopenko, Upul Senanayake
Date: 2014-06-30 00:00:00

Swarm dynamics is the study of collections of agents that interact with one
another without central control. In natural systems, insects, birds, fish and
other large mammals function in larger units to increase the overall fitness of
the individuals. Their behavior is coordinated through local interactions to
enhance mate selection, predator detection, migratory route identification and
so forth [Andersson and Wallander 2003; Buhl et al. 2006; Nagy et al. 2010;
Partridge 1982; Sumpter et al. 2008]. In artificial systems, swarms of
autonomous agents can augment human activities such as search and rescue, and
environmental monitoring by covering large areas with multiple nodes [Alami et
al. 2007; Caruso et al. 2008; Ogren et al. 2004; Paley et al. 2007; Sibley et
al. 2002]. In this paper, we explore the interplay between swarm dynamics,
covert leadership and theoretical information transfer. A leader is a member of
the swarm that acts upon information in addition to what is provided by local
interactions. Depending upon the leadership model, leaders can use their
external information either all the time or in response to local conditions
[Couzin et al. 2005; Sun et al. 2013]. A covert leader is a leader that is
treated no differently than others in the swarm, so leaders and followers
participate equally in whatever interaction model is used [Rossi et al. 2007].
In this study, we use theoretical information transfer as a means of analyzing
swarm interactions to explore whether or not it is possible to distinguish
between followers and leaders based on interactions within the swarm. We find
that covert leaders can be distinguished from followers in a swarm because they
receive less transfer entropy than followers.
Human Communication Systems Evolve by Cultural Selection
Authors: Nicolas Fay, Monica Tamariz, T. Mark Ellison, Dale Barr
Date: 2014-06-29 00:00:00

Human communication systems, such as language, evolve culturally; their
components undergo reproduction and variation. However, a role for selection in
cultural evolutionary dynamics is less clear. Often neutral evolution (also
known as 'drift') models, are used to explain the evolution of human
communication systems, and cultural evolution more generally. Under this
account, cultural change is unbiased: for instance, vocabulary, baby names and
pottery designs have been found to spread through random copying.
  While drift is the null hypothesis for models of cultural evolution it does
not always adequately explain empirical results. Alternative models include
cultural selection, which assumes variant adoption is biased. Theoretical
models of human communication argue that during conversation interlocutors are
biased to adopt the same labels and other aspects of linguistic representation
(including prosody and syntax). This basic alignment mechanism has been
extended by computer simulation to account for the emergence of linguistic
conventions. When agents are biased to match the linguistic behavior of their
interlocutor, a single variant can propagate across an entire population of
interacting computer agents. This behavior-matching account operates at the
level of the individual. We call it the Conformity-biased model. Under a
different selection account, called content-biased selection, functional
selection or replicator selection, variant adoption depends upon the intrinsic
value of the particular variant (e.g., ease of learning or use). This second
alternative account operates at the level of the cultural variant. Following
Boyd and Richerson we call it the Content-biased model. The present paper tests
the drift model and the two biased selection models' ability to explain the
spread of communicative signal variants in an experimental micro-society.
Model Consistency of Partly Smooth Regularizers
Authors: Samuel Vaiter, Gabriel Peyré, Jalal M. Fadili
Date: 2014-05-05 00:00:00
Tasks: model, Model Selection

This paper studies least-square regression penalized with partly smooth
convex regularizers. This class of functions is very large and versatile
allowing to promote solutions conforming to some notion of low-complexity.
Indeed, they force solutions of variational problems to belong to a
low-dimensional manifold (the so-called model) which is stable under small
perturbations of the function. This property is crucial to make the underlying
low-complexity model robust to small noise. We show that a generalized
"irrepresentable condition" implies stable model selection under small noise
perturbations in the observations and the design matrix, when the
regularization parameter is tuned proportionally to the noise level. This
condition is shown to be almost a necessary condition. We then show that this
condition implies model consistency of the regularized estimator. That is, with
a probability tending to one as the number of measurements increases, the
regularized estimator belongs to the correct low-dimensional model manifold.
This work unifies and generalizes several previous ones, where model
consistency is known to hold for sparse, group sparse, total variation and
low-rank regularizations.
Exploring Task Mappings on Heterogeneous MPSoCs using a Bias-Elitist Genetic Algorithm
Authors: Wei Quan, Andy D. Pimentel
Date: 2014-06-29 00:00:00

Exploration of task mappings plays a crucial role in achieving high
performance in heterogeneous multi-processor system-on-chip (MPSoC) platforms.
The problem of optimally mapping a set of tasks onto a set of given
heterogeneous processors for maximal throughput has been known, in general, to
be NP-complete. The problem is further exacerbated when multiple applications
(i.e., bigger task sets) and the communication between tasks are also
considered. Previous research has shown that Genetic Algorithms (GA) typically
are a good choice to solve this problem when the solution space is relatively
small. However, when the size of the problem space increases, classic genetic
algorithms still suffer from the problem of long evolution times. To address
this problem, this paper proposes a novel bias-elitist genetic algorithm that
is guided by domain-specific heuristics to speed up the evolution process.
Experimental results reveal that our proposed algorithm is able to handle large
scale task mapping problems and produces high-quality mapping solutions in only
a short time period.
Estimating the distribution of Galaxy Morphologies on a continuous space
Authors: Giuseppe Vinci, Peter Freeman, Jeffrey Newman, Larry Wasserman, Christopher Genovese
Date: 2014-06-29 00:00:00
Tasks: Dictionary Learning

The incredible variety of galaxy shapes cannot be summarized by human defined
discrete classes of shapes without causing a possibly large loss of
information. Dictionary learning and sparse coding allow us to reduce the high
dimensional space of shapes into a manageable low dimensional continuous vector
space. Statistical inference can be done in the reduced space via probability
distribution estimation and manifold estimation.
Fusion Based Holistic Road Scene Understanding
Authors: Wenqi Huang, Xiaojin Gong
Date: 2014-06-29 00:00:00
Tasks: Clustering, Image Segmentation, Object, road scene understanding, Scene Understanding, Segmentation, Semantic Segmentation

This paper addresses the problem of holistic road scene understanding based
on the integration of visual and range data. To achieve the grand goal, we
propose an approach that jointly tackles object-level image segmentation and
semantic region labeling within a conditional random field (CRF) framework.
Specifically, we first generate semantic object hypotheses by clustering 3D
points, learning their prior appearance models, and using a deep learning
method for reasoning their semantic categories. The learned priors, together
with spatial and geometric contexts, are incorporated in CRF. With this
formulation, visual and range data are fused thoroughly, and moreover, the
coupled segmentation and semantic labeling problem can be inferred via Graph
Cuts. Our approach is validated on the challenging KITTI dataset that contains
diverse complicated road scenarios. Both quantitative and qualitative
evaluations demonstrate its effectiveness.
Jabalin: a Comprehensive Computational Model of Modern Standard Arabic Verbal Morphology Based on Traditional Arabic Prosody
Authors: Alicia Gonzalez Martinez, Susana Lopez Hervas, Doaa Samy, Carlos G. Arques, Antonio Moreno Sandoval
Date: 2014-06-29 00:00:00

The computational handling of Modern Standard Arabic is a challenge in the
field of natural language processing due to its highly rich morphology.
However, several authors have pointed out that the Arabic morphological system
is in fact extremely regular. The existing Arabic morphological analyzers have
exploited this regularity to variable extent, yet we believe there is still
some scope for improvement. Taking inspiration in traditional Arabic prosody,
we have designed and implemented a compact and simple morphological system
which in our opinion takes further advantage of the regularities encountered in
the Arabic morphological system. The output of the system is a large-scale
lexicon of inflected forms that has subsequently been used to create an Online
Interface for a morphological analyzer of Arabic verbs. The Jabalin Online
Interface is available at http://elvira.lllf.uam.es/jabalin/, hosted at the
LLI-UAM lab. The generation system is also available under a GNU GPL 3 license.
Missing Entries Matrix Approximation and Completion
Authors: Gil Shabat, Yaniv Shmueli, Amir Averbuch
Date: 2013-02-27 00:00:00
Tasks: Electrical Engineering, Matrix Completion

We describe several algorithms for matrix completion and matrix approximation
when only some of its entries are known. The approximation constraint can be
any whose approximated solution is known for the full matrix. For low rank
approximations, similar algorithms appears recently in the literature under
different names. In this work, we introduce new theorems for matrix
approximation and show that these algorithms can be extended to handle
different constraints such as nuclear norm, spectral norm, orthogonality
constraints and more that are different than low rank approximations. As the
algorithms can be viewed from an optimization point of view, we discuss their
convergence to global solution for the convex case. We also discuss the optimal
step size and show that it is fixed in each iteration. In addition, the derived
matrix completion flow is robust and does not require any parameters. This
matrix completion flow is applicable to different spectral minimizations and
can be applied to physics, mathematics and electrical engineering problems such
as data reconstruction of images and data coming from PDEs such as Helmholtz
equation used for electromagnetic waves.
An Efficient Hybrid CS and K-Means Algorithm for the Capacitated PMedian Problem
Authors: Hassan Gholami Mazinan, Gholam Reza Ahmadi, Erfan Khaji
Date: 2014-06-29 00:00:00

Capacitated p-median problem (CPMP) is an important variation of facility
location problem in which p capacitated medians are economically selected to
serve a set of demand vertices so that the total assigned demand to each of the
candidate medians must not exceed its capacity. This paper surveys and analyses
the combination of Cuckoo Search and K-Means algorithms to solve the CPMP. In
order to check for quality and validity of the suggestive method, we compared
the final solution produced over the two test problems of Osman and
Christofides, each of which including 10 sample tests. According to the
results, the suggested meta-heuristic algorithm shows superiority over the rest
known algorithms in this field as all the best known solutions in the first
problem set, and several sample sets in the second problem set have been
improved within reasonable periods of time.
Graphical structure of conditional independencies in determinantal point processes
Authors: Tvrtko Tadić
Date: 2014-06-21 00:00:00
Tasks: BIG-bench Machine Learning, Point Processes

Determinantal point process have recently been used as models in machine
learning and this has raised questions regarding the characterizations of
conditional independence. In this paper we investigate characterizations of
conditional independence. We describe some conditional independencies through
the conditions on the kernel of a determinantal point process, and show many
can be obtained using the graph induced by a kernel of the $L$-ensemble.
Learning Nonlinear Functions Using Regularized Greedy Forest
Authors: Rie Johnson, Tong Zhang
Date: 2011-09-05 00:00:00

We consider the problem of learning a forest of nonlinear decision rules with
general loss functions. The standard methods employ boosted decision trees such
as Adaboost for exponential loss and Friedman's gradient boosting for general
loss. In contrast to these traditional boosting algorithms that treat a tree
learner as a black box, the method we propose directly learns decision forests
via fully-corrective regularized greedy search using the underlying forest
structure. Our method achieves higher accuracy and smaller models than gradient
boosting (and Adaboost with exponential loss) on many datasets.
Contrastive Feature Induction for Efficient Structure Learning of Conditional Random Fields
Authors: Ni Lao, Jun Zhu
Date: 2014-06-28 00:00:00
Tasks: feature selection, Relational Reasoning

Structure learning of Conditional Random Fields (CRFs) can be cast into an
L1-regularized optimization problem. To avoid optimizing over a fully linked
model, gain-based or gradient-based feature selection methods start from an
empty model and incrementally add top ranked features to it. However, for
high-dimensional problems like statistical relational learning, training time
of these incremental methods can be dominated by the cost of evaluating the
gain or gradient of a large collection of candidate features. In this study we
propose a fast feature evaluation algorithm called Contrastive Feature
Induction (CFI), which only evaluates a subset of features that involve both
variables with high signals (deviation from mean) and variables with high
errors (residue). We prove that the gradient of candidate features can be
represented solely as a function of signals and errors, and that CFI is an
efficient approximation of gradient-based evaluation methods. Experiments on
synthetic and real data sets show competitive learning speed and accuracy of
CFI on pairwise CRFs, compared to state-of-the-art structure learning methods
such as full optimization over all features, and Grafting.
Learning to Deblur
Authors: Christian J. Schuler, Michael Hirsch, Stefan Harmeling, Bernhard Schölkopf
Date: 2014-06-28 00:00:00
Tasks: Image Deconvolution

We describe a learning-based approach to blind image deconvolution. It uses a
deep layered architecture, parts of which are borrowed from recent work on
neural network learning, and parts of which incorporate computations that are
specific to image deconvolution. The system is trained end-to-end on a set of
artificially generated training examples, enabling competitive performance in
blind deconvolution, both with respect to quality and runtime.
Comparison of SVM Optimization Techniques in the Primal
Authors: Jonathan Katzman, Diane Duros
Date: 2014-06-28 00:00:00
Tasks: Sentiment Analysis

This paper examines the efficacy of different optimization techniques in a
primal formulation of a support vector machine (SVM). Three main techniques are
compared. The dataset used to compare all three techniques was the Sentiment
Analysis on Movie Reviews dataset, from kaggle.com.
Credibility Adjusted Term Frequency: A Supervised Term Weighting Scheme for Sentiment Analysis and Text Classification
Authors: Yoon Kim, Owen Zhang
Date: 2014-05-14 00:00:00
Proceeding: WS 2014 6
Tasks: General Classification, Sentiment Analysis, text-classification, Text Classification

We provide a simple but novel supervised weighting scheme for adjusting term
frequency in tf-idf for sentiment analysis and text classification. We compare
our method to baseline weighting schemes and find that it outperforms them on
multiple benchmarks. The method is robust and works well on both snippets and
longer documents.
Intelligent Emergency Message Broadcasting in VANET Using PSO
Authors: Ghassan Samara, Tareq Alhmiedat
Date: 2014-06-28 00:00:00

The new type of Mobile Ad hoc Network which is called Vehicular Ad hoc
Networks (VANET) created a fertile environment for research. In this research,
a protocol Particle Swarm Optimization Contention Based Broadcast (PCBB) is
proposed, for fast andeffective dissemination of emergency messages within a
geographical area to distribute the emergency message and achieve the safety
system, this research will help the VANET system to achieve its safety goals in
intelligent and efficient way.
Exponentially Increasing the Capacity-to-Computation Ratio for Conditional Computation in Deep Learning
Authors: Kyunghyun Cho, Yoshua Bengio
Date: 2014-06-28 00:00:00

Many state-of-the-art results obtained with deep networks are achieved with
the largest models that could be trained, and if more computation power was
available, we might be able to exploit much larger datasets in order to improve
generalization ability. Whereas in learning algorithms such as decision trees
the ratio of capacity (e.g., the number of parameters) to computation is very
favorable (up to exponentially more parameters than computation), the ratio is
essentially 1 for deep neural networks. Conditional computation has been
proposed as a way to increase the capacity of a deep neural network without
increasing the amount of computation required, by activating some parameters
and computation "on-demand", on a per-example basis. In this note, we propose a
novel parametrization of weight matrices in neural networks which has the
potential to increase up to exponentially the ratio of the number of parameters
to computation. The proposed approach is based on turning on some parameters
(weight matrices) when specific bit patterns of hidden unit activations are
obtained. In order to better control for the overfitting that might result, we
propose a parametrization that is tree-structured, where each node of the tree
corresponds to a prefix of a sequence of sign bits, or gating units, associated
with hidden units.
Optimal Demand Response Using Device Based Reinforcement Learning
Authors: Zheng Wen, Daniel O'Neill, Hamid Reza Maei
Date: 2014-01-08 00:00:00
Tasks: energy management, Management, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Demand response (DR) for residential and small commercial buildings is
estimated to account for as much as 65% of the total energy savings potential
of DR, and previous work shows that a fully automated Energy Management System
(EMS) is a necessary prerequisite to DR in these areas. In this paper, we
propose a novel EMS formulation for DR problems in these sectors. Specifically,
we formulate a fully automated EMS's rescheduling problem as a reinforcement
learning (RL) problem, and argue that this RL problem can be approximately
solved by decomposing it over device clusters. Compared with existing
formulations, our new formulation (1) does not require explicitly modeling the
user's dissatisfaction on job rescheduling, (2) enables the EMS to
self-initiate jobs, (3) allows the user to initiate more flexible requests and
(4) has a computational complexity linear in the number of devices. We also
demonstrate the simulation results of applying Q-learning, one of the most
popular and classical RL algorithms, to a representative example.
Stock Market Prediction from WSJ: Text Mining via Sparse Matrix Factorization
Authors: Felix Ming Fai Wong, Zhenming Liu, Mung Chiang
Date: 2014-06-27 00:00:00
Tasks: Articles, Stock Market Prediction

We revisit the problem of predicting directional movements of stock prices
based on news articles: here our algorithm uses daily articles from The Wall
Street Journal to predict the closing stock prices on the same day. We propose
a unified latent space model to characterize the "co-movements" between stock
prices and news articles. Unlike many existing approaches, our new model is
able to simultaneously leverage the correlations: (a) among stock prices, (b)
among news articles, and (c) between stock prices and news articles. Thus, our
model is able to make daily predictions on more than 500 stocks (most of which
are not even mentioned in any news article) while having low complexity. We
carry out extensive backtesting on trading strategies based on our algorithm.
The result shows that our model has substantially better accuracy rate (55.7%)
compared to many widely used algorithms. The return (56%) and Sharpe ratio due
to a trading strategy based on our model are also much higher than baseline
indices.