pwc-archive/papers-with-abstracts huggingface dataset:
Temporal Topic Modeling to Assess Associations between News Trends and Infectious Disease Outbreaks
Authors: Saurav Ghosh, Prithwish Chakraborty, Elaine O. Nsoesie, Emily Cohn, Sumiko R. Mekaru, John S. Brownstein, Naren Ramakrishnan
Date: 2016-06-01 00:00:00
Tasks: Articles, Time Series, Time Series Analysis, Time Series Regression, Topic Models

In retrospective assessments, internet news reports have been shown to
capture early reports of unknown infectious disease transmission prior to
official laboratory confirmation. In general, media interest and reporting
peaks and wanes during the course of an outbreak. In this study, we quantify
the extent to which media interest during infectious disease outbreaks is
indicative of trends of reported incidence. We introduce an approach that uses
supervised temporal topic models to transform large corpora of news articles
into temporal topic trends. The key advantages of this approach include,
applicability to a wide range of diseases, and ability to capture disease
dynamics - including seasonality, abrupt peaks and troughs. We evaluated the
method using data from multiple infectious disease outbreaks reported in the
United States of America (U.S.), China and India. We noted that temporal topic
trends extracted from disease-related news reports successfully captured the
dynamics of multiple outbreaks such as whooping cough in U.S. (2012), dengue
outbreaks in India (2013) and China (2014). Our observations also suggest that
efficient modeling of temporal topic trends using time-series regression
techniques can estimate disease case counts with increased precision before
official reports by health organizations.
Black-box $α$-divergence Minimization
Authors: José Miguel Hernández-Lobato, Yingzhen Li, Mark Rowland, Daniel Hernández-Lobato, Thang Bui, Richard E. Turner
Date: 2015-11-10 00:00:00
Tasks: General Classification, regression

Black-box alpha (BB-$\alpha$) is a new approximate inference method based on
the minimization of $\alpha$-divergences. BB-$\alpha$ scales to large datasets
because it can be implemented using stochastic gradient descent. BB-$\alpha$
can be applied to complex probabilistic models with little effort since it only
requires as input the likelihood function and its gradients. These gradients
can be easily obtained using automatic differentiation. By changing the
divergence parameter $\alpha$, the method is able to interpolate between
variational Bayes (VB) ($\alpha \rightarrow 0$) and an algorithm similar to
expectation propagation (EP) ($\alpha = 1$). Experiments on probit regression
and neural network regression and classification problems show that BB-$\alpha$
with non-standard settings of $\alpha$, such as $\alpha = 0.5$, usually
produces better predictions than with $\alpha \rightarrow 0$ (VB) or $\alpha =
1$ (EP).
Scaling Submodular Maximization via Pruned Submodularity Graphs
Authors: Tianyi Zhou, Hua Ouyang, Yi Chang, Jeff Bilmes, Carlos Guestrin
Date: 2016-06-01 00:00:00
Tasks: Video Summarization

We propose a new random pruning method (called "submodular sparsification
(SS)") to reduce the cost of submodular maximization. The pruning is applied
via a "submodularity graph" over the $n$ ground elements, where each directed
edge is associated with a pairwise dependency defined by the submodular
function. In each step, SS prunes a $1-1/\sqrt{c}$ (for $c>1$) fraction of the
nodes using weights on edges computed based on only a small number ($O(\log
n)$) of randomly sampled nodes. The algorithm requires $\log_{\sqrt{c}}n$ steps
with a small and highly parallelizable per-step computation. An accuracy-speed
tradeoff parameter $c$, set as $c = 8$, leads to a fast shrink rate
$\sqrt{2}/4$ and small iteration complexity $\log_{2\sqrt{2}}n$. Analysis shows
that w.h.p., the greedy algorithm on the pruned set of size $O(\log^2 n)$ can
achieve a guarantee similar to that of processing the original dataset. In news
and video summarization tasks, SS is able to substantially reduce both
computational costs and memory usage, while maintaining (or even slightly
exceeding) the quality of the original (and much more costly) greedy algorithm.
Short Communication on QUIST: A Quick Clustering Algorithm
Authors: Sherenaz W. Al-Haj Baddar
Date: 2016-06-01 00:00:00
Tasks: Clustering

In this short communication we introduce the quick clustering algorithm
(QUIST), an efficient hierarchical clustering algorithm based on sorting. QUIST
is a poly-logarithmic divisive clustering algorithm that does not assume the
number of clusters, and/or the cluster size to be known ahead of time. It is
also insensitive to the original ordering of the input.
A PAC RL Algorithm for Episodic POMDPs
Authors: Zhaohan Daniel Guo, Shayan Doroudi, Emma Brunskill
Date: 2016-05-25 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Many interesting real world domains involve reinforcement learning (RL) in
partially observable environments. Efficient learning in such domains is
important, but existing sample complexity bounds for partially observable RL
are at least exponential in the episode length. We give, to our knowledge, the
first partially observable RL algorithm with a polynomial bound on the number
of episodes on which the algorithm may not achieve near-optimal performance.
Our algorithm is suitable for an important class of episodic POMDPs. Our
approach builds on recent advances in method of moments for latent variable
model estimation.
Community Recovery in Graphs with Locality
Authors: Yuxin Chen, Govinda Kamath, Changho Suh, David Tse
Date: 2016-02-11 00:00:00

Motivated by applications in domains such as social networks and
computational biology, we study the problem of community recovery in graphs
with locality. In this problem, pairwise noisy measurements of whether two
nodes are in the same community or different communities come mainly or
exclusively from nearby nodes rather than uniformly sampled between all nodes
pairs, as in most existing models. We present an algorithm that runs nearly
linearly in the number of measurements and which achieves the information
theoretic limit for exact recovery.
Conversational Contextual Cues: The Case of Personalization and History for Response Ranking
Authors: Rami Al-Rfou, Marc Pickett, Javier Snaider, Yun-Hsuan Sung, Brian Strope, Ray Kurzweil
Date: 2016-06-01 00:00:00

We investigate the task of modeling open-domain, multi-turn, unstructured,
multi-participant, conversational dialogue. We specifically study the effect of
incorporating different elements of the conversation. Unlike previous efforts,
which focused on modeling messages and responses, we extend the modeling to
long context and participant's history. Our system does not rely on handwritten
rules or engineered features; instead, we train deep neural networks on a large
conversational dataset. In particular, we exploit the structure of Reddit
comments and posts to extract 2.1 billion messages and 133 million
conversations. We evaluate our models on the task of predicting the next
response in a conversation, and we find that modeling both context and
participants improves prediction accuracy.
Decoding Emotional Experience through Physiological Signal Processing
Authors: Maria S. Perez-Rosero, Behnaz Rezaei, Murat Akcakaya, Sarah Ostadabbas
Date: 2016-06-01 00:00:00
Tasks: Dimensionality Reduction, Emotion Recognition, General Classification

There is an increasing consensus among re- searchers that making a computer
emotionally intelligent with the ability to decode human affective states would
allow a more meaningful and natural way of human-computer interactions (HCIs).
One unobtrusive and non-invasive way of recognizing human affective states
entails the exploration of how physiological signals vary under different
emotional experiences. In particular, this paper explores the correlation
between autonomically-mediated changes in multimodal body signals and discrete
emotional states. In order to fully exploit the information in each modality,
we have provided an innovative classification approach for three specific
physiological signals including Electromyogram (EMG), Blood Volume Pressure
(BVP) and Galvanic Skin Response (GSR). These signals are analyzed as inputs to
an emotion recognition paradigm based on fusion of a series of weak learners.
Our proposed classification approach showed 88.1% recognition accuracy, which
outperformed the conventional Support Vector Machine (SVM) classifier with 17%
accuracy improvement. Furthermore, in order to avoid information redundancy and
the resultant over-fitting, a feature reduction method is proposed based on a
correlation analysis to optimize the number of features required for training
and validating each weak learner. Results showed that despite the feature space
dimensionality reduction from 27 to 18 features, our methodology preserved the
recognition accuracy of about 85.0%. This reduction in complexity will get us
one step closer towards embedding this human emotion encoder in the wireless
and wearable HCI platforms.
Variational inference for Monte Carlo objectives
Authors: Andriy Mnih, Danilo J. Rezende
Date: 2016-02-22 00:00:00
Tasks: Variational Inference

Recent progress in deep latent variable models has largely been driven by the
development of flexible and scalable variational inference methods. Variational
training of this type involves maximizing a lower bound on the log-likelihood,
using samples from the variational posterior to compute the required gradients.
Recently, Burda et al. (2016) have derived a tighter lower bound using a
multi-sample importance sampling estimate of the likelihood and showed that
optimizing it yields models that use more of their capacity and achieve higher
likelihoods. This development showed the importance of such multi-sample
objectives and explained the success of several related approaches.
  We extend the multi-sample approach to discrete latent variables and analyze
the difficulty encountered when estimating the gradients involved. We then
develop the first unbiased gradient estimator designed for importance-sampled
objectives and evaluate it at training generative and structured output
prediction models. The resulting estimator, which is based on low-variance
per-sample learning signals, is both simpler and more effective than the NVIL
estimator proposed for the single-sample variational objective, and is
competitive with the currently used biased estimators.
Delay and Cooperation in Nonstochastic Bandits
Authors: Nicolo' Cesa-Bianchi, Claudio Gentile, Yishay Mansour, Alberto Minora
Date: 2016-02-15 00:00:00

We study networks of communicating learning agents that cooperate to solve a
common nonstochastic bandit problem. Agents use an underlying communication
network to get messages about actions selected by other agents, and drop
messages that took more than $d$ hops to arrive, where $d$ is a delay
parameter. We introduce \textsc{Exp3-Coop}, a cooperative version of the {\sc
Exp3} algorithm and prove that with $K$ actions and $N$ agents the average
per-agent regret after $T$ rounds is at most of order $\sqrt{\bigl(d+1 +
\tfrac{K}{N}\alpha_{\le d}\bigr)(T\ln K)}$, where $\alpha_{\le d}$ is the
independence number of the $d$-th power of the connected communication graph
$G$. We then show that for any connected graph, for $d=\sqrt{K}$ the regret
bound is $K^{1/4}\sqrt{T}$, strictly better than the minimax regret $\sqrt{KT}$
for noncooperating agents. More informed choices of $d$ lead to bounds which
are arbitrarily close to the full information minimax regret $\sqrt{T\ln K}$
when $G$ is dense. When $G$ has sparse components, we show that a variant of
\textsc{Exp3-Coop}, allowing agents to choose their parameters according to
their centrality in $G$, strictly improves the regret. Finally, as a by-product
of our analysis, we provide the first characterization of the minimax regret
for bandit learning with delay.
A structured argumentation framework for detaching conditional obligations
Authors: Mathieu Beirlaen, Christian Straßer
Date: 2016-06-01 00:00:00

We present a general formal argumentation system for dealing with the
detachment of conditional obligations. Given a set of facts, constraints, and
conditional obligations, we answer the question whether an unconditional
obligation is detachable by considering reasons for and against its detachment.
For the evaluation of arguments in favor of detaching obligations we use a
Dung-style argumentation-theoretical semantics. We illustrate the modularity of
the general framework by considering some extensions, and we compare the
framework to some related approaches from the literature.
Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits
Authors: Vasilis Syrgkanis, Haipeng Luo, Akshay Krishnamurthy, Robert E. Schapire
Date: 2016-06-01 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Multi-Armed Bandits

We give an oracle-based algorithm for the adversarial contextual bandit
problem, where either contexts are drawn i.i.d. or the sequence of contexts is
known a priori, but where the losses are picked adversarially. Our algorithm is
computationally efficient, assuming access to an offline optimization oracle,
and enjoys a regret of order $O((KT)^{\frac{2}{3}}(\log N)^{\frac{1}{3}})$,
where $K$ is the number of actions, $T$ is the number of iterations and $N$ is
the number of baseline policies. Our result is the first to break the
$O(T^{\frac{3}{4}})$ barrier that is achieved by recently introduced
algorithms. Breaking this barrier was left as a major open problem. Our
analysis is based on the recent relaxation based approach of (Rakhlin and
Sridharan, 2016).
Optimal Best Arm Identification with Fixed Confidence
Authors: Aurélien Garivier, Emilie Kaufmann
Date: 2016-02-15 00:00:00

We give a complete characterization of the complexity of best-arm
identification in one-parameter bandit problems. We prove a new, tight lower
bound on the sample complexity. We propose the `Track-and-Stop' strategy, which
we prove to be asymptotically optimal. It consists in a new sampling rule
(which tracks the optimal proportions of arm draws highlighted by the lower
bound) and in a stopping rule named after Chernoff, for which we give a new
analysis.
Automatic tagging using deep convolutional neural networks
Authors: Keunwoo Choi, George Fazekas, Mark Sandler
Date: 2016-06-01 00:00:00
Tasks: Music Tagging

We present a content-based automatic music tagging algorithm using fully
convolutional neural networks (FCNs). We evaluate different architectures
consisting of 2D convolutional layers and subsampling layers only. In the
experiments, we measure the AUC-ROC scores of the architectures with different
complexities and input types using the MagnaTagATune dataset, where a 4-layer
architecture shows state-of-the-art performance with mel-spectrogram input.
Furthermore, we evaluated the performances of the architectures with varying
the number of layers on a larger dataset (Million Song Dataset), and found that
deeper models outperformed the 4-layer architecture. The experiments show that
mel-spectrogram is an effective time-frequency representation for automatic
tagging and that more complex models benefit from more training data.
Improved Parsing for Argument-Clusters Coordination
Authors: Jessica Ficler, Yoav Goldberg
Date: 2016-06-01 00:00:00
Proceeding: ACL 2016 8

Syntactic parsers perform poorly in prediction of Argument-Cluster
Coordination (ACC). We change the PTB representation of ACC to be more suitable
for learning by a statistical PCFG parser, affecting 125 trees in the training
set. Training on the modified trees yields a slight improvement in EVALB scores
on sections 22 and 23. The main evaluation is on a corpus of 4th grade science
exams, in which ACC structures are prevalent. On this corpus, we obtain an
impressive x2.7 improvement in recovering ACC structures compared to a parser
trained on the original PTB trees.
Multi-Label Zero-Shot Learning via Concept Embedding
Authors: Ubai Sandouk, Ke Chen
Date: 2016-06-01 00:00:00
Tasks: Multi-label zero-shot learning, Zero-Shot Learning

Zero Shot Learning (ZSL) enables a learning model to classify instances of an
unseen class during training. While most research in ZSL focuses on
single-label classification, few studies have been done in multi-label ZSL,
where an instance is associated with a set of labels simultaneously, due to the
difficulty in modeling complex semantics conveyed by a set of labels. In this
paper, we propose a novel approach to multi-label ZSL via concept embedding
learned from collections of public users' annotations of multimedia. Thanks to
concept embedding, multi-label ZSL can be done by efficiently mapping an
instance input features onto the concept embedding space in a similar manner
used in single-label ZSL. Moreover, our semantic learning model is capable of
embedding an out-of-vocabulary label by inferring its meaning from its
co-occurring labels. Thus, our approach allows both seen and unseen labels
during the concept embedding learning to be used in the aforementioned instance
mapping, which makes multi-label ZSL more flexible and suitable for real
applications. Experimental results of multi-label ZSL on images and music
tracks suggest that our approach outperforms a state-of-the-art multi-label ZSL
model and can deal with a scenario involving out-of-vocabulary labels without
re-training the semantics learning model.
Finding Singular Features
Authors: Christopher Genovese, Marco Perone-Pacifico, Isabella Verdinelli, Larry Wasserman
Date: 2016-06-01 00:00:00
Tasks: Clustering

We present a method for finding high density, low-dimensional structures in
noisy point clouds. These structures are sets with zero Lebesgue measure with
respect to the $D$-dimensional ambient space and belong to a $d<D$ dimensional
space. We call them "singular features." Hunting for singular features
corresponds to finding unexpected or unknown structures hidden in point clouds
belonging to $\R^D$. Our method outputs well defined sets of dimensions $d<D$.
Unlike spectral clustering, the method works well in the presence of noise. We
show how to find singular features by first finding ridges in the estimated
density, followed by a filtering step based on the eigenvalues of the Hessian
of the density.
On a Topic Model for Sentences
Authors: Georgios Balikas, Massih-Reza Amini, Marianne Clausel
Date: 2016-06-01 00:00:00
Tasks: General Classification, model, text-classification, Text Classification, Topic Models

Probabilistic topic models are generative models that describe the content of
documents by discovering the latent topics underlying them. However, the
structure of the textual input, and for instance the grouping of words in
coherent text spans such as sentences, contains much information which is
generally lost with these models. In this paper, we propose sentenceLDA, an
extension of LDA whose goal is to overcome this limitation by incorporating the
structure of the text in the generative and inference processes. We illustrate
the advantages of sentenceLDA by comparing it with LDA using both intrinsic
(perplexity) and extrinsic (text classification) evaluation tasks on different
text collections.
The local convexity of solving systems of quadratic equations
Authors: Chris D. White, Sujay Sanghavi, Rachel Ward
Date: 2015-06-25 00:00:00
Tasks: Quantum State Tomography

This paper considers the recovery of a rank $r$ positive semidefinite matrix
$X X^T\in\mathbb{R}^{n\times n}$ from $m$ scalar measurements of the form $y_i
:= a_i^T X X^T a_i$ (i.e., quadratic measurements of $X$). Such problems arise
in a variety of applications, including covariance sketching of
high-dimensional data streams, quadratic regression, quantum state tomography,
among others. A natural approach to this problem is to minimize the loss
function $f(U) = \sum_i (y_i - a_i^TUU^Ta_i)^2$ which has an entire manifold of
solutions given by $\{XO\}_{O\in\mathcal{O}_r}$ where $\mathcal{O}_r$ is the
orthogonal group of $r\times r$ orthogonal matrices; this is {\it non-convex}
in the $n\times r$ matrix $U$, but methods like gradient descent are simple and
easy to implement (as compared to semidefinite relaxation approaches).
  In this paper we show that once we have $m \geq C nr \log^2(n)$ samples from
isotropic gaussian $a_i$, with high probability {\em (a)} this function admits
a dimension-independent region of {\em local strong convexity} on lines
perpendicular to the solution manifold, and {\em (b)} with an additional
polynomial factor of $r$ samples, a simple spectral initialization will land
within the region of convexity with high probability. Together, this implies
that gradient descent with initialization (but no re-sampling) will converge
linearly to the correct $X$, up to an orthogonal transformation. We believe
that this general technique (local convexity reachable by spectral
initialization) should prove applicable to a broader class of nonconvex
optimization problems.
Hyperspectral Subspace Identification Using SURE
Authors: Behnood Rasti, Magnus O. Ulfarsson, Johannes R. Sveinsson
Date: 2016-06-01 00:00:00

Remote sensing hyperspectral sensors collect large volumes of high
dimensional spectral and spatial data. However, due to spectral and spatial
redundancy the true hyperspectral signal lies on a subspace of much lower
dimension than the original data. The identification of the signal subspace is
a very important first step for most hyperspectral algorithms. In this paper we
investigate the important problem of identifying the hyperspectral signal
subspace by minimizing the mean squared error (MSE) between the true signal and
an estimate of the signal. Since the MSE is uncomputable in practice, due to
its dependency on the true signal, we propose a method based on the Stein's
unbiased risk estimator (SURE) that provides an unbiased estimate of the MSE.
The resulting method is simple and fully automatic and we evaluate it using
both simulated and real hyperspectral data sets. Experimental results shows
that our proposed method compares well to recent state-of-the-art subspace
identification methods.
Exploiting N-Best Hypotheses to Improve an SMT Approach to Grammatical Error Correction
Authors: Duc Tam Hoang, Shamil Chollampatt, Hwee Tou Ng
Date: 2016-06-01 00:00:00
Tasks: Grammatical Error Correction, Machine Translation, Translation

Grammatical error correction (GEC) is the task of detecting and correcting
grammatical errors in texts written by second language learners. The
statistical machine translation (SMT) approach to GEC, in which sentences
written by second language learners are translated to grammatically correct
sentences, has achieved state-of-the-art accuracy. However, the SMT approach is
unable to utilize global context. In this paper, we propose a novel approach to
improve the accuracy of GEC, by exploiting the n-best hypotheses generated by
an SMT approach. Specifically, we build a classifier to score the edits in the
n-best hypotheses. The classifier can be used to select appropriate edits or
re-rank the n-best hypotheses. We apply these methods to a state-of-the-art GEC
system that uses the SMT approach. Our experiments show that our methods
achieve statistically significant improvements in accuracy over the best
published results on a benchmark test dataset on GEC.