pwc-archive/papers-with-abstracts huggingface dataset:
Testing Poisson Binomial Distributions
Authors: Jayadev Acharya, Constantinos Daskalakis
Date: 2014-10-13 00:00:00

A Poisson Binomial distribution over $n$ variables is the distribution of the
sum of $n$ independent Bernoullis. We provide a sample near-optimal algorithm
for testing whether a distribution $P$ supported on $\{0,...,n\}$ to which we
have sample access is a Poisson Binomial distribution, or far from all Poisson
Binomial distributions. The sample complexity of our algorithm is $O(n^{1/4})$
to which we provide a matching lower bound. We note that our sample complexity
improves quadratically upon that of the naive "learn followed by tolerant-test"
approach, while instance optimal identity testing [VV14] is not applicable
since we are looking to simultaneously test against a whole family of
distributions.
NILE: Fast Natural Language Processing for Electronic Health Records
Authors: Sheng Yu, Tianrun Cai, Tianxi Cai
Date: 2013-11-23 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), Negation

Objective: Narrative text in Electronic health records (EHR) contain rich information for medical and data science studies. This paper introduces the design and performance of Narrative Information Linear Extraction (NILE), a natural language processing (NLP) package for EHR analysis that we share with the medical informatics community. Methods: NILE uses a modified prefix-tree search algorithm for named entity recognition, which can detect prefix and suffix sharing. The semantic analyses are implemented as rule-based finite state machines. Analyses include negation, location, modification, family history, and ignoring. Result: The processing speed of NILE is hundreds to thousands times faster than existing NLP software for medical text. The accuracy of presence analysis of NILE is on par with the best performing models on the 2010 i2b2/VA NLP challenge data. Conclusion: The speed, accuracy, and being able to operate via API make NILE a valuable addition to the NLP software for medical informatics and data science.
Enhanced Higgs to $τ^+τ^-$ Searches with Deep Learning
Authors: Pierre Baldi, Peter Sadowski, Daniel Whiteson
Date: 2014-10-13 00:00:00
Tasks: Bayesian Optimization, Deep Learning

The Higgs boson is thought to provide the interaction that imparts mass to
the fundamental fermions, but while measurements at the Large Hadron Collider
(LHC) are consistent with this hypothesis, current analysis techniques lack the
statistical power to cross the traditional 5$\sigma$ significance barrier
without more data. \emph{Deep learning} techniques have the potential to
increase the statistical power of this analysis by \emph{automatically}
learning complex, high-level data representations. In this work, deep neural
networks are used to detect the decay of the Higgs to a pair of tau leptons. A
Bayesian optimization algorithm is used to tune the network architecture and
training algorithm hyperparameters, resulting in a deep network of eight
non-linear processing layers that improves upon the performance of shallow
classifiers even without the use of features specifically engineered by
physicists for this application. The improvement in discovery significance is
equivalent to an increase in the accumulated dataset of 25\%.
Computing Topology Preservation of RBF Transformations for Landmark-Based Image Registration
Authors: R. Cavoretto, A. De Rossi, H. Qiao, B. Quatember, W. Recheis, M. Mayr
Date: 2014-10-13 00:00:00
Tasks: Image Registration

In image registration, a proper transformation should be topology preserving.
Especially for landmark-based image registration, if the displacement of one
landmark is larger enough than those of neighbourhood landmarks, topology
violation will be occurred. This paper aim to analyse the topology preservation
of some Radial Basis Functions (RBFs) which are used to model deformations in
image registration. Mat\'{e}rn functions are quite common in the statistic
literature (see, e.g. \cite{Matern86,Stein99}). In this paper, we use them to
solve the landmark-based image registration problem. We present the topology
preservation properties of RBFs in one landmark and four landmarks model
respectively. Numerical results of three kinds of Mat\'{e}rn transformations
are compared with results of Gaussian, Wendland's, and Wu's functions.
Homotopy equivalence of finite digital images
Authors: Jason Haarmann, Meg P. Murphy, Casey S. Peters, P. Christopher Staecker
Date: 2014-08-11 00:00:00
Tasks: Relation

For digital images, there is an established homotopy equivalence relation
which parallels that of classical topology. Many classical homotopy equivalence
invariants, such as the Euler characteristic and the homology groups, do not
remain invariants in the digital setting. This paper develops a numerical
digital homotopy invariant and begins to catalog all possible connected digital
images on a small number of points, up to homotopy equivalence.
Fast Multilevel Support Vector Machines
Authors: Talayeh Razzaghi, Ilya Safro
Date: 2014-10-13 00:00:00
Tasks: imbalanced classification

Solving different types of optimization models (including parameters fitting)
for support vector machines on large-scale training data is often an expensive
computational task. This paper proposes a multilevel algorithmic framework that
scales efficiently to very large data sets. Instead of solving the whole
training set in one optimization process, the support vectors are obtained and
gradually refined at multiple levels of coarseness of the data. The proposed
framework includes: (a) construction of hierarchy of large-scale data coarse
representations, and (b) a local processing of updating the hyperplane
throughout this hierarchy. Our multilevel framework substantially improves the
computational time without loosing the quality of classifiers. The algorithms
are demonstrated for both regular and weighted support vector machines.
Experimental results are presented for balanced and imbalanced classification
problems. Quality improvement on several imbalanced data sets has been
observed.
Mining Block I/O Traces for Cache Preloading with Sparse Temporal Non-parametric Mixture of Multivariate Poisson
Authors: Lavanya Sita Tekumalla, Chiranjib Bhattacharyya
Date: 2014-10-13 00:00:00
Tasks: Clustering

Existing caching strategies, in the storage domain, though well suited to exploit short range spatio-temporal patterns, are unable to leverage long-range motifs for improving hitrates. Motivated by this, we investigate novel Bayesian non-parametric modeling(BNP) techniques for count vectors, to capture long range correlations for cache preloading, by mining Block I/O traces. Such traces comprise of a sequence of memory accesses that can be aggregated into high-dimensional sparse correlated count vector sequences. While there are several state of the art BNP algorithms for clustering and their temporal extensions for prediction, there has been no work on exploring these for correlated count vectors. Our first contribution addresses this gap by proposing a DP based mixture model of Multivariate Poisson (DP-MMVP) and its temporal extension(HMM-DP-MMVP) that captures the full covariance structure of multivariate count data. However, modeling full covariance structure for count vectors is computationally expensive, particularly for high dimensional data. Hence, we exploit sparsity in our count vectors, and as our main contribution, introduce the Sparse DP mixture of multivariate Poisson(Sparse-DP-MMVP), generalizing our DP-MMVP mixture model, also leading to more efficient inference. We then discuss a temporal extension to our model for cache preloading. We take the first step towards mining historical data, to capture long range patterns in storage traces for cache preloading. Experimentally, we show a dramatic improvement in hitrates on benchmark traces and lay the groundwork for further research in storage domain to reduce latencies using data mining techniques to capture long range motifs.
Neural Hypernetwork Approach for Pulmonary Embolism diagnosis
Authors: Matteo Rucco, David M. S. Rodrigues, Emanuela Merelli, Jeffrey H. Johnson, Lorenzo Falsetti, Cinzia Nitti, Aldo Salvi
Date: 2014-09-19 00:00:00
Tasks: Diagnostic, Relation, Topological Data Analysis

This work introduces an integrative approach based on Q-analysis with machine
learning. The new approach, called Neural Hypernetwork, has been applied to a
case study of pulmonary embolism diagnosis. The objective of the application of
neural hyper-network to pulmonary embolism (PE) is to improve diagnose for
reducing the number of CT-angiography needed. Hypernetworks, based on
topological simplicial complex, generalize the concept of two-relation to
many-body relation. Furthermore, Hypernetworks provide a significant
generalization of network theory, enabling the integration of relational
structure, logic and analytic dynamics. Another important results is that
Q-analysis stays close to the data, while other approaches manipulate data,
projecting them into metric spaces or applying some filtering functions to
highlight the intrinsic relations. A pulmonary embolism (PE) is a blockage of
the main artery of the lung or one of its branches, frequently fatal. Our study
uses data on 28 diagnostic features of 1,427 people considered to be at risk of
PE. The resulting neural hypernetwork correctly recognized 94% of those
developing a PE. This is better than previous results that have been obtained
with other methods (statistical selection of features, partial least squares
regression, topological data analysis in a metric space).
Propagation Kernels
Authors: Marion Neumann, Roman Garnett, Christian Bauckhage, Kristian Kersting
Date: 2014-10-13 00:00:00

We introduce propagation kernels, a general graph-kernel framework for
efficiently measuring the similarity of structured data. Propagation kernels
are based on monitoring how information spreads through a set of given graphs.
They leverage early-stage distributions from propagation schemes such as random
walks to capture structural information encoded in node labels, attributes, and
edge information. This has two benefits. First, off-the-shelf propagation
schemes can be used to naturally construct kernels for many graph types,
including labeled, partially labeled, unlabeled, directed, and attributed
graphs. Second, by leveraging existing efficient and informative propagation
schemes, propagation kernels can be considerably faster than state-of-the-art
approaches without sacrificing predictive performance. We will also show that
if the graphs at hand have a regular structure, for instance when modeling
image or video data, one can exploit this regularity to scale the kernel
computation to large databases of graphs with thousands of nodes. We support
our contributions by exhaustive experiments on a number of real-world graphs
from a variety of application domains.
Tag Relevance Fusion for Social Image Retrieval
Authors: Xirong Li
Date: 2014-10-13 00:00:00
Tasks: Image Retrieval, Retrieval, TAG

Due to the subjective nature of social tagging, measuring the relevance of
social tags with respect to the visual content is crucial for retrieving the
increasing amounts of social-networked images. Witnessing the limit of a single
measurement of tag relevance, we introduce in this paper tag relevance fusion
as an extension to methods for tag relevance estimation. We present a
systematic study, covering tag relevance fusion in early and late stages, and
in supervised and unsupervised settings. Experiments on a large present-day
benchmark set show that tag relevance fusion leads to better image retrieval.
Moreover, unsupervised tag relevance fusion is found to be practically as
effective as supervised tag relevance fusion, but without the need of any
training efforts. This finding suggests the potential of tag relevance fusion
for real-world deployment.
Sentiment Analysis based on User Tag for Traditional Chinese Medicine in Weibo
Authors: Junhui Shen, Peiyan Zhu, Rui Fan, Wei Tan
Date: 2014-10-13 00:00:00
Tasks: Cultural Vocal Bursts Intensity Prediction, Sentiment Analysis, TAG

With the acceptance of Western culture and science, Traditional Chinese
Medicine (TCM) has become a controversial issue in China. So, it's important to
study the public's sentiment and opinion on TCM. The rapid development of
online social network, such as twitter, make it convenient and efficient to
sample hundreds of millions of people for the aforementioned sentiment study.
To the best of our knowledge, the present work is the first attempt that
applies sentiment analysis to the domain of TCM on Sina Weibo (a twitter-like
microblogging service in China). In our work, firstly we collect tweets topic
about TCM from Sina Weibo, and label the tweets as supporting TCM and opposing
TCM automatically based on user tag. Then, a support vector machine classifier
has been built to predict the sentiment of TCM tweets without labels. Finally,
we present a method to adjust the classifier result. The performance of
F-measure attained with our method is 97%.
Markov Random Fields and Mass Spectra Discrimination
Authors: Ao Kong, Robert Azencott
Date: 2014-10-13 00:00:00

For mass spectra acquired from cancer patients by MALDI or SELDI techniques,
automated discrimination between cancer types or stages has often been
implemented by machine learnings. These techniques typically generate
"black-box" classifiers, which are difficult to interpret biologically. We
develop new and efficient signature discovery algorithms leading to
interpretable signatures combining the discriminating power of explicitly
selected small groups of biomarkers, identified by their m/z ratios. Our
approach is based on rigorous stochastic modeling of "homogeneous" datasets of
mass spectra by a versatile class of parameterized Markov Random Fields. We
present detailed algorithms validated by precise theoretical results. We also
outline the successful tests of our approach to generate efficient explicit
signatures for six benchmark discrimination tasks, based on mass spectra
acquired from colorectal cancer patients, as well as from ovarian cancer
patients.
PAC-Bayesian AUC classification and scoring
Authors: James Ridgway, Pierre Alquier, Nicolas Chopin, Feng Liang
Date: 2014-10-07 00:00:00
Proceeding: NeurIPS 2014 12
Tasks: Classification, feature selection, General Classification

We develop a scoring and classification procedure based on the PAC-Bayesian
approach and the AUC (Area Under Curve) criterion. We focus initially on the
class of linear score functions. We derive PAC-Bayesian non-asymptotic bounds
for two types of prior for the score parameters: a Gaussian prior, and a
spike-and-slab prior; the latter makes it possible to perform feature
selection. One important advantage of our approach is that it is amenable to
powerful Bayesian computational tools. We derive in particular a Sequential
Monte Carlo algorithm, as an efficient method which may be used as a gold
standard, and an Expectation-Propagation algorithm, as a much faster but
approximate method. We also extend our method to a class of non-linear score
functions, essentially leading to a nonparametric procedure, by considering a
Gaussian process prior.
Learning without Concentration for General Loss Functions
Authors: Shahar Mendelson
Date: 2014-10-13 00:00:00

We study prediction and estimation problems using empirical risk
minimization, relative to a general convex loss function. We obtain sharp error
rates even when concentration is false or is very restricted, for example, in
heavy-tailed scenarios. Our results show that the error rate depends on two
parameters: one captures the intrinsic complexity of the class, and essentially
leads to the error rate in a noise-free (or realizable) problem; the other
measures interactions between class members the target and the loss, and is
dominant when the problem is far from realizable. We also explain how one may
deal with outliers by choosing the loss in a way that is calibrated to the
intrinsic complexity of the class and to the noise-level of the problem (the
latter is measured by the distance between the target and the class).
Multi-Scale Local Shape Analysis and Feature Selection in Machine Learning Applications
Authors: Paul Bendich, Ellen Gasparovic, John Harer, Rauf Izmailov, Linda Ness
Date: 2014-10-13 00:00:00
Tasks: BIG-bench Machine Learning, feature selection, General Classification

We introduce a method called multi-scale local shape analysis, or MLSA, for
extracting features that describe the local structure of points within a
dataset. The method uses both geometric and topological features at multiple
levels of granularity to capture diverse types of local information for
subsequent machine learning algorithms operating on the dataset. Using
synthetic and real dataset examples, we demonstrate significant performance
improvement of classification algorithms constructed for these datasets with
correspondingly augmented features.
Graph matching: relax or not?
Authors: Yonathan Aflalo, Alex Bronstein, Ron Kimmel
Date: 2014-01-29 00:00:00
Tasks: Graph Matching, valid

We consider the problem of exact and inexact matching of weighted undirected
graphs, in which a bijective correspondence is sought to minimize a quadratic
weight disagreement. This computationally challenging problem is often relaxed
as a convex quadratic program, in which the space of permutations is replaced
by the space of doubly-stochastic matrices. However, the applicability of such
a relaxation is poorly understood. We define a broad class of friendly graphs
characterized by an easily verifiable spectral property. We prove that for
friendly graphs, the convex relaxation is guaranteed to find the exact
isomorphism or certify its inexistence. This result is further extended to
approximately isomorphic graphs, for which we develop an explicit bound on the
amount of weight disagreement under which the relaxation is guaranteed to find
the globally optimal approximate isomorphism. We also show that in many cases,
the graph matching problem can be further harmlessly relaxed to a convex
quadratic program with only n separable linear equality constraints, which is
substantially more efficient than the standard relaxation involving 2n equality
and n^2 inequality constraints. Finally, we show that our results are still
valid for unfriendly graphs if additional information in the form of seeds or
attributes is allowed, with the latter satisfying an easy to verify spectral
characteristic.
Sparse Matrix-based Random Projection for Classification
Authors: Weizhi Lu, Weiyu Li, Kidiyo Kpalma, Joseph Ronsin
Date: 2013-12-12 00:00:00
Tasks: Classification, Dimensionality Reduction, feature selection, General Classification

As a typical dimensionality reduction technique, random projection can be
simply implemented with linear projection, while maintaining the pairwise
distances of high-dimensional data with high probability. Considering this
technique is mainly exploited for the task of classification, this paper is
developed to study the construction of random matrix from the viewpoint of
feature selection, rather than of traditional distance preservation. This
yields a somewhat surprising theoretical result, that is, the sparse random
matrix with exactly one nonzero element per column, can present better feature
selection performance than other more dense matrices, if the projection
dimension is sufficiently large (namely, not much smaller than the number of
feature elements); otherwise, it will perform comparably to others. For random
projection, this theoretical result implies considerable improvement on both
complexity and performance, which is widely confirmed with the classification
experiments on both synthetic data and real data.
Machine Learning Techniques in Cognitive Radio Networks
Authors: Peter Hossain, Adaulfo Komisarczuk, Garin Pawetczak, Sarah Van Dijk, Isabella Axelsen
Date: 2014-10-12 00:00:00
Tasks: BIG-bench Machine Learning

Cognitive radio is an intelligent radio that can be programmed and configured
dynamically to fully use the frequency resources that are not used by licensed
users. It defines the radio devices that are capable of learning and adapting
to their transmission to the external radio environment, which means it has
some kind of intelligence for monitoring the radio environment, learning the
environment and make smart decisions. In this paper, we are reviewing some
examples of the usage of machine learning techniques in cognitive radio
networks for implementing the intelligent radio.
Relational Linear Programs
Authors: Kristian Kersting, Martin Mladenov, Pavel Tokmakov
Date: 2014-10-12 00:00:00

We propose relational linear programming, a simple framework for combing
linear programs (LPs) and logic programs. A relational linear program (RLP) is
a declarative LP template defining the objective and the constraints through
the logical concepts of objects, relations, and quantified variables. This
allows one to express the LP objective and constraints relationally for a
varying number of individuals and relations among them without enumerating
them. Together with a logical knowledge base, effectively a logical program
consisting of logical facts and rules, it induces a ground LP. This ground LP
is solved using lifted linear programming. That is, symmetries within the
ground LP are employed to reduce its dimensionality, if possible, and the
reduced program is solved using any off-the-shelf LP solver. In contrast to
mainstream LP template languages like AMPL, which features a mixture of
declarative and imperative programming styles, RLP's relational nature allows a
more intuitive representation of optimization problems over relational domains.
We illustrate this empirically by experiments on approximate inference in
Markov logic networks using LP relaxations, on solving Markov decision
processes, and on collective inference using LP support vector machines.
Hierarchical models for neural population dynamics in the presence of non-stationarity
Authors: Mijung Park, Jakob H. Macke
Date: 2014-10-12 00:00:00
Tasks: Variational Inference

Neural population activity often exhibits rich variability and temporal
structure. This variability is thought to arise from single-neuron
stochasticity, neural dynamics on short time-scales, as well as from
modulations of neural firing properties on long time-scales, often referred to
as "non-stationarity". To better understand the nature of co-variability in
neural circuits and their impact on cortical information processing, we need
statistical models that are able to capture multiple sources of variability on
different time-scales. Here, we introduce a hierarchical statistical model of
neural population activity which models both neural population dynamics as well
as inter-trial modulations in firing rates. In addition, we extend the model to
allow us to capture non-stationarities in the population dynamics itself (i.e.,
correlations across neurons).
  We develop variational inference methods for learning model parameters, and
demonstrate that the method can recover non-stationarities in both average
firing rates and correlation structure. Applied to neural population recordings
from anesthetized macaque primary visual cortex, our models provide a better
account of the structure of neural firing than stationary dynamics models.
Tree-Structure Bayesian Compressive Sensing for Video
Authors: Xin Yuan, Patrick Llull, David J. Brady, Lawrence Carin
Date: 2014-10-12 00:00:00
Tasks: Compressive Sensing, Video Reconstruction

A Bayesian compressive sensing framework is developed for video
reconstruction based on the color coded aperture compressive temporal imaging
(CACTI) system. By exploiting the three dimension (3D) tree structure of the
wavelet and Discrete Cosine Transformation (DCT) coefficients, a Bayesian
compressive sensing inversion algorithm is derived to reconstruct (up to 22)
color video frames from a single monochromatic compressive measurement. Both
simulated and real datasets are adopted to verify the performance of the
proposed algorithm.