pwc-archive/papers-with-abstracts huggingface dataset:
Learning, Visualizing, and Exploiting a Model for the Intrinsic Value of a Batted Ball
Authors: Glenn Healey
Date: 2016-02-21 00:00:00

We present an algorithm for learning the intrinsic value of a batted ball in
baseball. This work addresses the fundamental problem of separating the value
of a batted ball at contact from factors such as the defense, weather, and
ballpark that can affect its observed outcome. The algorithm uses a Bayesian
model to construct a continuous mapping from a vector of batted ball parameters
to an intrinsic measure defined as the expected value of a linear weights
representation for run value. A kernel method is used to build nonparametric
estimates for the component probability density functions in Bayes theorem from
a set of over one hundred thousand batted ball measurements recorded by the
HITf/x system during the 2014 major league baseball (MLB) season.
Cross-validation is used to determine the optimal vector of smoothing
parameters for the density estimates. Properties of the mapping are visualized
by considering reduced-dimension subsets of the batted ball parameter space. We
use the mapping to derive statistics for intrinsic quality of contact for
batters and pitchers which have the potential to improve the accuracy of player
models and forecasting systems. We also show that the new approach leads to a
simple automated measure of contact-adjusted defense and provides insight into
the impact of environmental variables on batted balls.
Efficient functional ANOVA through wavelet-domain Markov groves
Authors: Li Ma, Jacopo Soriano
Date: 2016-02-12 00:00:00
Tasks: Clustering

We introduce a wavelet-domain functional analysis of variance (fANOVA) method
based on a Bayesian hierarchical model. The factor effects are modeled through
a spike-and-slab mixture at each location-scale combination along with a
normal-inverse-Gamma (NIG) conjugate setup for the coefficients and errors. A
graphical model called the Markov grove (MG) is designed to jointly model the
spike-and-slab statuses at all location-scale combinations, which incorporates
the clustering of each factor effect in the wavelet-domain thereby allowing
borrowing of strength across location and scale. The posterior of this NIG-MG
model is analytically available through a pyramid algorithm of the same
computational complexity as Mallat's pyramid algorithm for discrete wavelet
transform, i.e., linear in both the number of observations and the number of
locations. Posterior probabilities of factor contributions can also be computed
through pyramid recursion, and exact samples from the posterior can be drawn
without MCMC. We investigate the performance of our method through extensive
simulation and show that it outperforms existing wavelet-domain fANOVA methods
in a variety of common settings. We apply the method to analyzing the orthosis
data.
Distributed Private Online Learning for Social Big Data Computing over Data Center Networks
Authors: Chencheng Li, Pan Zhou, Yingxue Zhou, Kaigui Bian, Tao Jiang, Susanto Rahardja
Date: 2016-02-21 00:00:00
Tasks: Cloud Computing, Privacy Preserving

With the rapid growth of Internet technologies, cloud computing and social
networks have become ubiquitous. An increasing number of people participate in
social networks and massive online social data are obtained. In order to
exploit knowledge from copious amounts of data obtained and predict social
behavior of users, we urge to realize data mining in social networks. Almost
all online websites use cloud services to effectively process the large scale
of social data, which are gathered from distributed data centers. These data
are so large-scale, high-dimension and widely distributed that we propose a
distributed sparse online algorithm to handle them. Additionally,
privacy-protection is an important point in social networks. We should not
compromise the privacy of individuals in networks, while these social data are
being learned for data mining. Thus we also consider the privacy problem in
this article. Our simulations shows that the appropriate sparsity of data would
enhance the performance of our algorithm and the privacy-preserving method does
not significantly hurt the performance of the proposed algorithm.
Computational Narrative Intelligence: A Human-Centered Goal for Artificial Intelligence
Authors: Mark O. Riedl
Date: 2016-02-21 00:00:00
Tasks: BIG-bench Machine Learning

Narrative intelligence is the ability to craft, tell, understand, and respond
affectively to stories. We argue that instilling artificial intelligences with
computational narrative intelligence affords a number of applications
beneficial to humans. We lay out some of the machine learning challenges
necessary to solve to achieve computational narrative intelligence. Finally, we
argue that computational narrative is a practical step towards machine
enculturation, the teaching of sociocultural values to machines.
Social planning for social HRI
Authors: Liz Sonenberg, Tim Miller, Adrian Pearce, Paolo Felli, Christian Muise, Frank Dignum
Date: 2016-02-21 00:00:00

Making a computational agent 'social' has implications for how it perceives
itself and the environment in which it is situated, including the ability to
recognise the behaviours of others. We point to recent work on social planning,
i.e. planning in settings where the social context is relevant in the
assessment of the beliefs and capabilities of others, and in making appropriate
choices of what to do next.
Bio-Inspired Human Action Recognition using Hybrid Max-Product Neuro-Fuzzy Classifier and Quantum-Behaved PSO
Authors: Bardia Yousefi, Chu Kiong Loo
Date: 2015-09-13 00:00:00
Tasks: Action Recognition, Form, Temporal Action Localization

Studies on computational neuroscience through functional magnetic resonance
imaging (fMRI) and following biological inspired system stated that human
action recognition in the brain of mammalian leads two distinct pathways in the
model, which are specialized for analysis of motion (optic flow) and form
information. Principally, we have defined a novel and robust form features
applying active basis model as form extractor in form pathway in the biological
inspired model. An unbalanced synergetic neural net-work classifies shapes and
structures of human objects along with tuning its attention parameter by
quantum particle swarm optimization (QPSO) via initiation of Centroidal Voronoi
Tessellations. These tools utilized and justified as strong tools for following
biological system model in form pathway. But the final decision has done by
combination of ultimate outcomes of both pathways via fuzzy inference which
increases novality of proposed model. Combination of these two brain pathways
is done by considering each feature sets in Gaussian membership functions with
fuzzy product inference method. Two configurations have been proposed for form
pathway: applying multi-prototype human action templates using two time
synergetic neural network for obtaining uniform template regarding each
actions, and second scenario that it uses abstracting human action in four
key-frames. Experimental results showed promising accuracy performance on
different datasets (KTH and Weizmann).
The Singularity May Never Be Near
Authors: Toby Walsh
Date: 2016-02-20 00:00:00

There is both much optimism and pessimism around artificial intelligence (AI)
today. The optimists are investing millions of dollars, and even in some cases
billions of dollars into AI. The pessimists, on the other hand, predict that AI
will end many things: jobs, warfare, and even the human race. Both the
optimists and the pessimists often appeal to the idea of a technological
singularity, a point in time where machine intelligence starts to run away, and
a new, more intelligent species starts to inhabit the earth. If the optimists
are right, this will be a moment that fundamentally changes our economy and our
society. If the pessimists are right, this will be a moment that also
fundamentally changes our economy and our society. It is therefore very
worthwhile spending some time deciding if either of them might be right.
Causes for Query Answers from Databases, Datalog Abduction and View-Updates: The Presence of Integrity Constraints
Authors: Babak Salimi, Leopoldo Bertossi
Date: 2016-02-20 00:00:00

Causality has been recently introduced in databases, to model, characterize
and possibly compute causes for query results (answers). Connections between
queryanswer causality, consistency-based diagnosis, database repairs (wrt.
integrity constraint violations), abductive diagnosis and the view-update
problem have been established. In this work we further investigate connections
between query-answer causality and abductive diagnosis and the view-update
problem. In this context, we also define and investigate the notion of
query-answer causality in the presence of integrity constraints.
Exploiting Anonymity in Approximate Linear Programming: Scaling to Large Multiagent MDPs (Extended Version)
Authors: Philipp Robbel, Frans A. Oliehoek, Mykel J. Kochenderfer
Date: 2015-11-29 00:00:00

Many exact and approximate solution methods for Markov Decision Processes
(MDPs) attempt to exploit structure in the problem and are based on
factorization of the value function. Especially multiagent settings, however,
are known to suffer from an exponential increase in value component sizes as
interactions become denser, meaning that approximation architectures are
restricted in the problem sizes and types they can handle. We present an
approach to mitigate this limitation for certain types of multiagent systems,
exploiting a property that can be thought of as "anonymous influence" in the
factored MDP. Anonymous influence summarizes joint variable effects efficiently
whenever the explicit representation of variable identity in the problem can be
avoided. We show how representational benefits from anonymity translate into
computational efficiencies, both for general variable elimination in a factor
graph but in particular also for the approximate linear programming solution to
factored MDPs. The latter allows to scale linear programming to factored MDPs
that were previously unsolvable. Our results are shown for the control of a
stochastic disease process over a densely connected graph with 50 nodes and 25
agents.
A Dataset for Improved RGBD-based Object Detection and Pose Estimation for Warehouse Pick-and-Place
Authors: Colin Rennie, Rahul Shome, Kostas E. Bekris, Alberto F. de Souza
Date: 2015-09-03 00:00:00
Tasks: Object, object-detection, Object Detection, Pose Estimation

An important logistics application of robotics involves manipulators that
pick-and-place objects placed in warehouse shelves. A critical aspect of this
task corre- sponds to detecting the pose of a known object in the shelf using
visual data. Solving this problem can be assisted by the use of an RGB-D
sensor, which also provides depth information beyond visual data. Nevertheless,
it remains a challenging problem since multiple issues need to be addressed,
such as low illumination inside shelves, clutter, texture-less and reflective
objects as well as the limitations of depth sensors. This paper provides a new
rich data set for advancing the state-of-the-art in RGBD- based 3D object pose
estimation, which is focused on the challenges that arise when solving
warehouse pick- and-place tasks. The publicly available data set includes
thousands of images and corresponding ground truth data for the objects used
during the first Amazon Picking Challenge at different poses and clutter
conditions. Each image is accompanied with ground truth information to assist
in the evaluation of algorithms for object detection. To show the utility of
the data set, a recent algorithm for RGBD-based pose estimation is evaluated in
this paper. Based on the measured performance of the algorithm on the data set,
various modifications and improvements are applied to increase the accuracy of
detection. These steps can be easily applied to a variety of different
methodologies for object pose detection and improve performance in the domain
of warehouse pick-and-place.
Context-guided diffusion for label propagation on graphs
Authors: Kwang In Kim, James Tompkin, Hanspeter Pfister, Christian Theobalt
Date: 2016-02-20 00:00:00
Proceeding: ICCV 2015 12

Existing approaches for diffusion on graphs, e.g., for label propagation, are
mainly focused on isotropic diffusion, which is induced by the commonly-used
graph Laplacian regularizer. Inspired by the success of diffusivity tensors for
anisotropic diffusion in image processing, we presents anisotropic diffusion on
graphs and the corresponding label propagation algorithm. We develop positive
definite diffusivity operators on the vector bundles of Riemannian manifolds,
and discretize them to diffusivity operators on graphs. This enables us to
easily define new robust diffusivity operators which significantly improve
semi-supervised learning performance over existing diffusion algorithms.
Burstiness Scale: a highly parsimonious model for characterizing random series of events
Authors: Rodrigo A. S. Alves, Renato Assunção, Pedro O. S. Vaz de Melo
Date: 2016-02-20 00:00:00

The problem to accurately and parsimoniously characterize random series of
events (RSEs) present in the Web, such as e-mail conversations or Twitter
hashtags, is not trivial. Reports found in the literature reveal two apparent
conflicting visions of how RSEs should be modeled. From one side, the
Poissonian processes, of which consecutive events follow each other at a
relatively regular time and should not be correlated. On the other side, the
self-exciting processes, which are able to generate bursts of correlated events
and periods of inactivities. The existence of many and sometimes conflicting
approaches to model RSEs is a consequence of the unpredictability of the
aggregated dynamics of our individual and routine activities, which sometimes
show simple patterns, but sometimes results in irregular rising and falling
trends. In this paper we propose a highly parsimonious way to characterize
general RSEs, namely the Burstiness Scale (BuSca) model. BuSca views each RSE
as a mix of two independent process: a Poissonian and a self-exciting one. Here
we describe a fast method to extract the two parameters of BuSca that,
together, gives the burstyness scale, which represents how much of the RSE is
due to bursty and viral effects. We validated our method in eight diverse and
large datasets containing real random series of events seen in Twitter, Yelp,
e-mail conversations, Digg, and online forums. Results showed that, even using
only two parameters, BuSca is able to accurately describe RSEs seen in these
diverse systems, what can leverage many applications.
Stratified Bayesian Optimization
Authors: Saul Toscano-Palmerin, Peter I. Frazier
Date: 2016-02-07 00:00:00
Tasks: Bayesian Optimization, global-optimization

We consider derivative-free black-box global optimization of expensive noisy
functions, when most of the randomness in the objective is produced by a few
influential scalar random inputs. We present a new Bayesian global optimization
algorithm, called Stratified Bayesian Optimization (SBO), which uses this
strong dependence to improve performance. Our algorithm is similar in spirit to
stratification, a technique from simulation, which uses strong dependence on a
categorical representation of the random input to reduce variance. We
demonstrate in numerical experiments that SBO outperforms state-of-the-art
Bayesian optimization benchmarks that do not leverage this dependence.
Study of all the periods of a Neuronal Recurrence Equation
Authors: Serge Alain Ebélé, Renè Ndoundam
Date: 2015-03-23 00:00:00
Tasks: All

We characterize the structure of the periods of a neuronal recurrence
equation. Firstly, we give a characterization of k-chains in 0-1 periodic
sequences. Secondly, we characterize the periods of all cycles of some neuronal
recurrence equation. Thirdly, we explain how these results can be used to
deduce the existence of the generalized period-halving bifurcation.
Text Matching as Image Recognition
Authors: Liang Pang, Yanyan Lan, Jiafeng Guo, Jun Xu, Shengxian Wan, Xue-Qi Cheng
Date: 2016-02-20 00:00:00
Tasks: Ad-Hoc Information Retrieval, Text Matching

Matching two texts is a fundamental problem in many natural language
processing tasks. An effective way is to extract meaningful matching patterns
from words, phrases, and sentences to produce the matching score. Inspired by
the success of convolutional neural network in image recognition, where neurons
can capture many complicated patterns based on the extracted elementary visual
patterns such as oriented edges and corners, we propose to model text matching
as the problem of image recognition. Firstly, a matching matrix whose entries
represent the similarities between words is constructed and viewed as an image.
Then a convolutional neural network is utilized to capture rich matching
patterns in a layer-by-layer way. We show that by resembling the compositional
hierarchies of patterns in image recognition, our model can successfully
identify salient signals such as n-gram and n-term matchings. Experimental
results demonstrate its superiority against the baselines.
The Segmented iHMM: A Simple, Efficient Hierarchical Infinite HMM
Authors: Ardavan Saeedi, Matthew Hoffman, Matthew Johnson, Ryan Adams
Date: 2016-02-20 00:00:00
Tasks: Segmentation, Time Series, Time Series Analysis

We propose the segmented iHMM (siHMM), a hierarchical infinite hidden Markov
model (iHMM) that supports a simple, efficient inference scheme. The siHMM is
well suited to segmentation problems, where the goal is to identify points at
which a time series transitions from one relatively stable regime to a new
regime. Conventional iHMMs often struggle with such problems, since they have
no mechanism for distinguishing between high- and low-level dynamics.
Hierarchical HMMs (HHMMs) can do better, but they require much more complex and
expensive inference algorithms. The siHMM retains the simplicity and efficiency
of the iHMM, but outperforms it on a variety of segmentation problems,
achieving performance that matches or exceeds that of a more complicated HHMM.
Learning Laplacian Matrix in Smooth Graph Signal Representations
Authors: Xiaowen Dong, Dorina Thanou, Pascal Frossard, Pierre Vandergheynst
Date: 2014-06-30 00:00:00
Tasks: Graph Learning

The construction of a meaningful graph plays a crucial role in the success of
many graph-based representations and algorithms for handling structured data,
especially in the emerging field of graph signal processing. However, a
meaningful graph is not always readily available from the data, nor easy to
define depending on the application domain. In particular, it is often
desirable in graph signal processing applications that a graph is chosen such
that the data admit certain regularity or smoothness on the graph. In this
paper, we address the problem of learning graph Laplacians, which is equivalent
to learning graph topologies, such that the input data form graph signals with
smooth variations on the resulting topology. To this end, we adopt a factor
analysis model for the graph signals and impose a Gaussian probabilistic prior
on the latent variables that control these signals. We show that the Gaussian
prior leads to an efficient representation that favors the smoothness property
of the graph signals. We then propose an algorithm for learning graphs that
enforces such property and is based on minimizing the variations of the signals
on the learned graph. Experiments on both synthetic and real world data
demonstrate that the proposed graph learning framework can efficiently infer
meaningful graph topologies from signal observations under the smoothness
prior.
Semi-parametric Order-based Generalized Multivariate Regression
Authors: Milad Kharratzadeh, Mark Coates
Date: 2016-02-19 00:00:00
Tasks: regression

In this paper, we consider a generalized multivariate regression problem
where the responses are monotonic functions of linear transformations of
predictors. We propose a semi-parametric algorithm based on the ordering of the
responses which is invariant to the functional form of the transformation
function. We prove that our algorithm, which maximizes the rank correlation of
responses and linear transformations of predictors, is a consistent estimator
of the true coefficient matrix. We also identify the rate of convergence and
show that the squared estimation error decays with a rate of $o(1/\sqrt{n})$.
We then propose a greedy algorithm to maximize the highly non-smooth objective
function of our model and examine its performance through extensive
simulations. Finally, we compare our algorithm with traditional multivariate
regression algorithms over synthetic and real data.
A Mutual Contamination Analysis of Mixed Membership and Partial Label Models
Authors: Julian Katz-Samuels, Clayton Scott
Date: 2016-02-19 00:00:00
Tasks: BIG-bench Machine Learning

Many machine learning problems can be characterized by mutual contamination
models. In these problems, one observes several random samples from different
convex combinations of a set of unknown base distributions. It is of interest
to decontaminate mutual contamination models, i.e., to recover the base
distributions either exactly or up to a permutation. This paper considers the
general setting where the base distributions are defined on arbitrary
probability spaces. We examine the decontamination problem in two mutual
contamination models that describe popular machine learning tasks: recovering
the base distributions up to a permutation in a mixed membership model, and
recovering the base distributions exactly in a partial label model for
classification. We give necessary and sufficient conditions for identifiability
of both mutual contamination models, algorithms for both problems in the
infinite and finite sample cases, and introduce novel proof techniques based on
affine geometry.
GAP Safe Screening Rules for Sparse-Group-Lasso
Authors: Eugene Ndiaye, Olivier Fercoq, Alexandre Gramfort, Joseph Salmon
Date: 2016-02-19 00:00:00

In high dimensional settings, sparse structures are crucial for efficiency,
either in term of memory, computation or performance. In some contexts, it is
natural to handle more refined structures than pure sparsity, such as for
instance group sparsity. Sparse-Group Lasso has recently been introduced in the
context of linear regression to enforce sparsity both at the feature level and
at the group level. We adapt to the case of Sparse-Group Lasso recent safe
screening rules that discard early in the solver irrelevant features/groups.
Such rules have led to important speed-ups for a wide range of iterative
methods. Thanks to dual gap computations, we provide new safe screening rules
for Sparse-Group Lasso and show significant gains in term of computing time for
a coordinate descent implementation.
LCSTS: A Large Scale Chinese Short Text Summarization Dataset
Authors: Baotian Hu, Qingcai Chen, Fangze Zhu
Date: 2015-06-19 00:00:00
Proceeding: EMNLP 2015 9
Tasks: Text Summarization

Automatic text summarization is widely regarded as the highly difficult
problem, partially because of the lack of large text summarization data set.
Due to the great challenge of constructing the large scale summaries for full
text, in this paper, we introduce a large corpus of Chinese short text
summarization dataset constructed from the Chinese microblogging website Sina
Weibo, which is released to the public
{http://icrc.hitsz.edu.cn/Article/show/139.html}. This corpus consists of over
2 million real Chinese short texts with short summaries given by the author of
each text. We also manually tagged the relevance of 10,666 short summaries with
their corresponding short texts. Based on the corpus, we introduce recurrent
neural network for the summary generation and achieve promising results, which
not only shows the usefulness of the proposed corpus for short text
summarization research, but also provides a baseline for further research on
this topic.