pwc-archive/papers-with-abstracts huggingface dataset:
A Hierarchical Approach for Joint Multi-view Object Pose Estimation and Categorization
Authors: Mete Ozay, Krzysztof Walas, Ales Leonardis
Date: 2015-03-04 00:00:00
Tasks: Distributed Optimization, Object, Object Categorization, Pose Estimation

We propose a joint object pose estimation and categorization approach which
extracts information about object poses and categories from the object parts
and compositions constructed at different layers of a hierarchical object
representation algorithm, namely Learned Hierarchy of Parts (LHOP). In the
proposed approach, we first employ the LHOP to learn hierarchical part
libraries which represent entity parts and compositions across different object
categories and views. Then, we extract statistical and geometric features from
the part realizations of the objects in the images in order to represent the
information about object pose and category at each different layer of the
hierarchy. Unlike the traditional approaches which consider specific layers of
the hierarchies in order to extract information to perform specific tasks, we
combine the information extracted at different layers to solve a joint object
pose estimation and categorization problem using distributed optimization
algorithms. We examine the proposed generative-discriminative learning approach
and the algorithms on two benchmark 2-D multi-view image datasets. The proposed
approach and the algorithms outperform state-of-the-art classification,
regression and feature extraction algorithms. In addition, the experimental
results shed light on the relationship between object categorization, pose
estimation and the part realizations observed at different layers of the
hierarchy.
Large Scale Purchase Prediction with Historical User Actions on B2C Online Retail Platform
Authors: Yuyu Zhang, Liang Pang, Lei Shi, Bin Wang
Date: 2014-08-27 00:00:00
Tasks: regression

This paper describes the solution of Bazinga Team for Tmall Recommendation
Prize 2014. With real-world user action data provided by Tmall, one of the
largest B2C online retail platforms in China, this competition requires to
predict future user purchases on Tmall website. Predictions are judged on
F1Score, which considers both precision and recall for fair evaluation. The
data set provided by Tmall contains more than half billion action records from
over ten million distinct users. Such massive data volume poses a big
challenge, and drives competitors to write every single program in MapReduce
fashion and run it on distributed cluster. We model the purchase prediction
problem as standard machine learning problem, and mainly employ regression and
classification methods as single models. Individual models are then aggregated
in a two-stage approach, using linear regression for blending, and finally a
linear ensemble of blended models. The competition is approaching the end but
still in running during writing this paper. In the end, our team achieves
F1Score 6.11 and ranks 7th (out of 7,276 teams in total).
Group-Sparse Model Selection: Hardness and Relaxations
Authors: Luca Baldassarre, Nirav Bhan, Volkan Cevher, Anastasios Kyrillidis, Siddhartha Satpathi
Date: 2013-03-13 00:00:00
Tasks: Compressive Sensing, model, Model Selection

Group-based sparsity models are proven instrumental in linear regression
problems for recovering signals from much fewer measurements than standard
compressive sensing. The main promise of these models is the recovery of
"interpretable" signals through the identification of their constituent groups.
In this paper, we establish a combinatorial framework for group-model selection
problems and highlight the underlying tractability issues. In particular, we
show that the group-model selection problem is equivalent to the well-known
NP-hard weighted maximum coverage problem (WMC). Leveraging a graph-based
understanding of group models, we describe group structures which enable
correct model selection in polynomial time via dynamic programming.
Furthermore, group structures that lead to totally unimodular constraints have
tractable discrete as well as convex relaxations. We also present a
generalization of the group-model that allows for within group sparsity, which
can be used to model hierarchical sparsity. Finally, we study the Pareto
frontier of group-sparse approximations for two tractable models, among which
the tree sparsity model, and illustrate selection and computation trade-offs
between our framework and the existing convex relaxations.
Zipf's law holds for phrases, not words
Authors: Jake Ryland Williams, Paul R. Lessard, Suma Desu, Eric Clark, James P. Bagrow, Christopher M. Danforth, Peter Sheridan Dodds
Date: 2014-06-19 00:00:00

With Zipf's law being originally and most famously observed for word
frequency, it is surprisingly limited in its applicability to human language,
holding over no more than three to four orders of magnitude before hitting a
clear break in scaling. Here, building on the simple observation that phrases
of one or more words comprise the most coherent units of meaning in language,
we show empirically that Zipf's law for phrases extends over as many as nine
orders of rank magnitude. In doing so, we develop a principled and scalable
statistical mechanical method of random text partitioning, which opens up a
rich frontier of rigorous text analysis via a rank ordering of mixed length
phrases.
Telling cause from effect in deterministic linear dynamical systems
Authors: Naji Shajarisales, Dominik Janzing, Bernhard Shoelkopf, Michel Besserve
Date: 2015-03-04 00:00:00
Tasks: Causal Discovery, Causal Inference, Time Series, Time Series Analysis

Inferring a cause from its effect using observed time series data is a major
challenge in natural and social sciences. Assuming the effect is generated by
the cause trough a linear system, we propose a new approach based on the
hypothesis that nature chooses the "cause" and the "mechanism that generates
the effect from the cause" independent of each other. We therefore postulate
that the power spectrum of the time series being the cause is uncorrelated with
the square of the transfer function of the linear filter generating the effect.
While most causal discovery methods for time series mainly rely on the noise,
our method relies on asymmetries of the power spectral density properties that
can be exploited even in the context of deterministic systems. We describe
mathematical assumptions in a deterministic model under which the causal
direction is identifiable with this approach. We also discuss the method's
performance under the additive noise model and its relationship to Granger
causality. Experiments show encouraging results on synthetic as well as
real-world data. Overall, this suggests that the postulate of Independence of
Cause and Mechanism is a promising principle for causal inference on empirical
time series.
Equilibrium Points of an AND-OR Tree: under Constraints on Probability
Authors: Toshio Suzuki, Yoshinao Niida
Date: 2014-01-31 00:00:00

We study a probability distribution d on the truth assignments to a uniform
binary AND-OR tree. Liu and Tanaka [2007, Inform. Process. Lett.] showed the
following: If d achieves the equilibrium among independent distributions (ID)
then d is an independent identical distribution (IID). We show a stronger form
of the above result. Given a real number r such that 0 < r < 1, we consider a
constraint that the probability of the root node having the value 0 is r. Our
main result is the following: When we restrict ourselves to IDs satisfying this
constraint, the above result of Liu and Tanaka still holds. The proof employs
clever tricks of induction. In particular, we show two fundamental
relationships between expected cost and probability in an IID on an OR-AND
tree: (1) The ratio of the cost to the probability (of the root having the
value 0) is a decreasing function of the probability x of the leaf. (2) The
ratio of derivative of the cost to the derivative of the probability is a
decreasing function of x, too.
Game-theoretic Approach for Non-Cooperative Planning
Authors: Jaume Jordán, Eva Onaindia
Date: 2015-03-04 00:00:00

When two or more self-interested agents put their plans to execution in the
same environment, conflicts may arise as a consequence, for instance, of a
common utilization of resources. In this case, an agent can postpone the
execution of a particular action, if this punctually solves the conflict, or it
can resort to execute a different plan if the agent's payoff significantly
diminishes due to the action deferral. In this paper, we present a
game-theoretic approach to non-cooperative planning that helps predict before
execution what plan schedules agents will adopt so that the set of strategies
of all agents constitute a Nash equilibrium. We perform some experiments and
discuss the solutions obtained with our game-theoretical approach, analyzing
how the conflicts between the plans determine the strategic behavior of the
agents.
The concept "altruism" for sociological research: from conceptualization to operationalization
Authors: Oleg V. Pavenkov, Vladimir G. Pavenkov, Mariia V. Rubtcova
Date: 2015-03-04 00:00:00

This article addresses the question of the relevant conceptualization of
{\guillemotleft}altruism{\guillemotright} in Russian from the perspective
sociological research operationalization. It investigates the spheres of social
application of the word {\guillemotleft}altruism{\guillemotright}, include
Russian equivalent {\guillemotleft}vzaimopomoshh`{\guillemotright} (mutual
help). The data for the study comes from Russian National Corpus (Russian). The
theoretical framework consists of Paul F. Lazarsfeld`s Theory of Sociological
Research Methodology and the Natural Semantic Metalanguage (NSM). Quantitative
analysis shows features in the representation of altruism in Russian that
sociologists need to know in the preparation of questionnaires, interview
guides and analysis of transcripts.
Novel Deviation Bounds for Mixture of Independent Bernoulli Variables with Application to the Missing Mass
Authors: Bahman Yari Saeed Khanloo
Date: 2014-02-25 00:00:00
Tasks: Learning Theory

In this paper, we are concerned with obtaining distribution-free
concentration inequalities for mixture of independent Bernoulli variables that
incorporate a notion of variance. Missing mass is the total probability mass
associated to the outcomes that have not been seen in a given sample which is
an important quantity that connects density estimates obtained from a sample to
the population for discrete distributions. Therefore, we are specifically
motivated to apply our method to study the concentration of missing mass -
which can be expressed as a mixture of Bernoulli - in a novel way.
  We not only derive - for the first time - Bernstein-like large deviation
bounds for the missing mass whose exponents behave almost linearly with respect
to deviation size, but also sharpen McAllester and Ortiz (2003) and Berend and
Kontorovich (2013) for large sample sizes in the case of small deviations which
is the most interesting case in learning theory. In the meantime, our approach
shows that the heterogeneity issue introduced in McAllester and Ortiz (2003) is
resolvable in the case of missing mass in the sense that one can use standard
inequalities but it may not lead to strong results. Thus, we postulate that our
results are general and can be applied to provide potentially sharp
Bernstein-like bounds under some constraints.
Connectedness of graphs and its application to connected matroids through covering-based rough sets
Authors: Aiping Huang, William Zhu
Date: 2013-12-16 00:00:00

Graph theoretical ideas are highly utilized by computer science fields
especially data mining. In this field, a data structure can be designed in the
form of tree. Covering is a widely used form of data representation in data
mining and covering-based rough sets provide a systematic approach to this type
of representation. In this paper, we study the connectedness of graphs through
covering-based rough sets and apply it to connected matroids. First, we present
an approach to inducing a covering by a graph, and then study the connectedness
of the graph from the viewpoint of the covering approximation operators.
Second, we construct a graph from a matroid, and find the matroid and the graph
have the same connectedness, which makes us to use covering-based rough sets to
study connected matroids. In summary, this paper provides a new approach to
studying graph theory and matroid theory.
On the Effects of Low-Quality Training Data on Information Extraction from Clinical Reports
Authors: Diego Marcheggiani, Fabrizio Sebastiani
Date: 2015-02-19 00:00:00

In the last five years there has been a flurry of work on information
extraction from clinical documents, i.e., on algorithms capable of extracting,
from the informal and unstructured texts that are generated during everyday
clinical practice, mentions of concepts relevant to such practice. Most of this
literature is about methods based on supervised learning, i.e., methods for
training an information extraction system from manually annotated examples.
While a lot of work has been devoted to devising learning methods that generate
more and more accurate information extractors, no work has been devoted to
investigating the effect of the quality of training data on the learning
process. Low quality in training data often derives from the fact that the
person who has annotated the data is different from the one against whose
judgment the automatically annotated data must be evaluated. In this paper we
test the impact of such data quality issues on the accuracy of information
extraction systems as applied to the clinical domain. We do this by comparing
the accuracy deriving from training data annotated by the authoritative coder
(i.e., the one who has also annotated the test data, and by whose judgment we
must abide), with the accuracy deriving from training data annotated by a
different coder. The results indicate that, although the disagreement between
the two coders (as measured on the training set) is substantial, the difference
is (surprisingly enough) not always statistically significant.
Large Dimensional Analysis of Robust M-Estimators of Covariance with Outliers
Authors: David Morales-Jimenez, Romain Couillet, Matthew R. McKay
Date: 2015-03-04 00:00:00

A large dimensional characterization of robust M-estimators of covariance (or
scatter) is provided under the assumption that the dataset comprises
independent (essentially Gaussian) legitimate samples as well as arbitrary
deterministic samples, referred to as outliers. Building upon recent random
matrix advances in the area of robust statistics, we specifically show that the
so-called Maronna M-estimator of scatter asymptotically behaves similar to
well-known random matrices when the population and sample sizes grow together
to infinity. The introduction of outliers leads the robust estimator to behave
asymptotically as the weighted sum of the sample outer products, with a
constant weight for all legitimate samples and different weights for the
outliers. A fine analysis of this structure reveals importantly that the
propensity of the M-estimator to attenuate (or enhance) the impact of outliers
is mostly dictated by the alignment of the outliers with the inverse population
covariance matrix of the legitimate samples. Thus, robust M-estimators can
bring substantial benefits over more simplistic estimators such as the
per-sample normalized version of the sample covariance matrix, which is not
capable of differentiating the outlying samples. The analysis shows that,
within the class of Maronna's estimators of scatter, the Huber estimator is
most favorable for rejecting outliers. On the contrary, estimators more similar
to Tyler's scale invariant estimator (often preferred in the literature) run
the risk of inadvertently enhancing some outliers.
Bethe Learning of Conditional Random Fields via MAP Decoding
Authors: Kui Tang, Nicholas Ruozzi, David Belanger, Tony Jebara
Date: 2015-03-04 00:00:00
Tasks: Image Segmentation, Semantic Segmentation

Many machine learning tasks can be formulated in terms of predicting
structured outputs. In frameworks such as the structured support vector machine
(SVM-Struct) and the structured perceptron, discriminative functions are
learned by iteratively applying efficient maximum a posteriori (MAP) decoding.
However, maximum likelihood estimation (MLE) of probabilistic models over these
same structured spaces requires computing partition functions, which is
generally intractable. This paper presents a method for learning discrete
exponential family models using the Bethe approximation to the MLE. Remarkably,
this problem also reduces to iterative (MAP) decoding. This connection emerges
by combining the Bethe approximation with a Frank-Wolfe (FW) algorithm on a
convex dual objective which circumvents the intractable partition function. The
result is a new single loop algorithm MLE-Struct, which is substantially more
efficient than previous double-loop methods for approximate maximum likelihood
estimation. Our algorithm outperforms existing methods in experiments involving
image segmentation, matching problems from vision, and a new dataset of
university roommate assignments.
Low-dimensional Models in Spatio-Temporal Wind Speed Forecasting
Authors: Borhan M. Sanandaji, Akin Tascikaraoglu, Kameshwar Poolla, Pravin Varaiya
Date: 2015-03-04 00:00:00
Tasks: Compressive Sensing, Time Series, Time Series Analysis

Integrating wind power into the grid is challenging because of its random
nature. Integration is facilitated with accurate short-term forecasts of wind
power. The paper presents a spatio-temporal wind speed forecasting algorithm
that incorporates the time series data of a target station and data of
surrounding stations. Inspired by Compressive Sensing (CS) and
structured-sparse recovery algorithms, we claim that there usually exists an
intrinsic low-dimensional structure governing a large collection of stations
that should be exploited. We cast the forecasting problem as recovery of a
block-sparse signal $\boldsymbol{x}$ from a set of linear equations
$\boldsymbol{b} = A\boldsymbol{x}$ for which we propose novel structure-sparse
recovery algorithms. Results of a case study in the east coast show that the
proposed Compressive Spatio-Temporal Wind Speed Forecasting (CST-WSF) algorithm
significantly improves the short-term forecasts compared to a set of
widely-used benchmark models.
An Empirical Investigation of Catastrophic Forgetting in Gradient-Based Neural Networks
Authors: Ian J. Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, Yoshua Bengio
Date: 2013-12-21 00:00:00
Tasks: BIG-bench Machine Learning

Catastrophic forgetting is a problem faced by many machine learning models
and algorithms. When trained on one task, then trained on a second task, many
machine learning models "forget" how to perform the first task. This is widely
believed to be a serious problem for neural networks. Here, we investigate the
extent to which the catastrophic forgetting problem occurs for modern neural
networks, comparing both established and recent gradient-based training
algorithms and activation functions. We also examine the effect of the
relationship between the first task and the second task on catastrophic
forgetting. We find that it is always best to train using the dropout
algorithm--the dropout algorithm is consistently best at adapting to the new
task, remembering the old task, and has the best tradeoff curve between these
two extremes. We find that different tasks and relationships between tasks
result in very different rankings of activation function performance. This
suggests the choice of activation function should always be cross-validated.
Statistical modality tagging from rule-based annotations and crowdsourcing
Authors: Vinodkumar Prabhakaran, Michael Bloodgood, Mona Diab, Bonnie Dorr, Lori Levin, Christine D. Piatko, Owen Rambow, Benjamin Van Durme
Date: 2015-03-04 00:00:00
Proceeding: WS 2012 7

We explore training an automatic modality tagger. Modality is the attitude
that a speaker might have toward an event or state. One of the main hurdles for
training a linguistic tagger is gathering training data. This is particularly
problematic for training a tagger for modality because modality triggers are
sparse for the overwhelming majority of sentences. We investigate an approach
to automatically training a modality tagger where we first gathered sentences
based on a high-recall simple rule-based modality tagger and then provided
these sentences to Mechanical Turk annotators for further annotation. We used
the resulting set of training data to train a precise modality tagger using a
multi-class SVM that delivers good performance.
On Online Control of False Discovery Rate
Authors: Adel Javanmard, Andrea Montanari
Date: 2015-02-22 00:00:00

Multiple hypotheses testing is a core problem in statistical inference and
arises in almost every scientific field. Given a sequence of null hypotheses
$\mathcal{H}(n) = (H_1,..., H_n)$, Benjamini and Hochberg
\cite{benjamini1995controlling} introduced the false discovery rate (FDR)
criterion, which is the expected proportion of false positives among rejected
null hypotheses, and proposed a testing procedure that controls FDR below a
pre-assigned significance level. They also proposed a different criterion,
called mFDR, which does not control a property of the realized set of tests;
rather it controls the ratio of expected number of false discoveries to the
expected number of discoveries.
  In this paper, we propose two procedures for multiple hypotheses testing that
we will call "LOND" and "LORD". These procedures control FDR and mFDR in an
\emph{online manner}. Concretely, we consider an ordered --possibly infinite--
sequence of null hypotheses $\mathcal{H} = (H_1,H_2,H_3,...)$ where, at each
step $i$, the statistician must decide whether to reject hypothesis $H_i$
having access only to the previous decisions. To the best of our knowledge, our
work is the first that controls FDR in this setting. This model was introduced
by Foster and Stine \cite{alpha-investing} whose alpha-investing rule only
controls mFDR in online manner.
  In order to compare different procedures, we develop lower bounds on the
total discovery rate under the mixture model and prove that both LOND and LORD
have nearly linear number of discoveries. We further propose adjustment to LOND
to address arbitrary correlation among the $p$-values. Finally, we evaluate the
performance of our procedures on both synthetic and real data comparing them
with alpha-investing rule, Benjamin-Hochberg method and a Bonferroni procedure.
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification
Authors: Been Kim, Cynthia Rudin, Julie Shah
Date: 2015-03-03 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Classification, Clustering, General Classification

We present the Bayesian Case Model (BCM), a general framework for Bayesian
case-based reasoning (CBR) and prototype classification and clustering. BCM
brings the intuitive power of CBR to a Bayesian generative framework. The BCM
learns prototypes, the "quintessential" observations that best represent
clusters in a dataset, by performing joint inference on cluster labels,
prototypes and important features. Simultaneously, BCM pursues sparsity by
learning subspaces, the sets of features that play important roles in the
characterization of the prototypes. The prototype and subspace representation
provides quantitative benefits in interpretability while preserving
classification accuracy. Human subject experiments verify statistically
significant improvements to participants' understanding when using explanations
produced by BCM, compared to those given by prior art.