pwc-archive/papers-with-abstracts huggingface dataset:
DataVizard: Recommending Visual Presentations for Structured Data
Authors: Rema Ananthanarayanan, Pranay Kr. Lohia, Srikanta Bedathur
Date: 2017-11-14 00:00:00

Selecting the appropriate visual presentation of the data such that it
preserves the semantics of the underlying data and at the same time provides an
intuitive summary of the data is an important, often the final step of data
analytics. Unfortunately, this is also a step involving significant human
effort starting from selection of groups of columns in the structured results
from analytics stages, to the selection of right visualization by experimenting
with various alternatives. In this paper, we describe our \emph{DataVizard}
system aimed at reducing this overhead by automatically recommending the most
appropriate visual presentation for the structured result. Specifically, we
consider the following two scenarios: first, when one needs to visualize the
results of a structured query such as SQL; and the second, when one has
acquired a data table with an associated short description (e.g., tables from
the Web). Using a corpus of real-world database queries (and their results) and
a number of statistical tables crawled from the Web, we show that DataVizard is
capable of recommending visual presentations with high accuracy. We also
present the results of a user survey that we conducted in order to assess user
views of the suitability of the presented charts vis-a-vis the plain text
captions of the data.
Near-optimal sample complexity for convex tensor completion
Authors: Navid Ghadermarzy, Yaniv Plan, Özgür Yılmaz
Date: 2017-11-14 00:00:00

We analyze low rank tensor completion (TC) using noisy measurements of a
subset of the tensor. Assuming a rank-$r$, order-$d$, $N \times N \times \cdots
\times N$ tensor where $r=O(1)$, the best sampling complexity that was achieved
is $O(N^{\frac{d}{2}})$, which is obtained by solving a tensor nuclear-norm
minimization problem. However, this bound is significantly larger than the
number of free variables in a low rank tensor which is $O(dN)$. In this paper,
we show that by using an atomic-norm whose atoms are rank-$1$ sign tensors, one
can obtain a sample complexity of $O(dN)$. Moreover, we generalize the matrix
max-norm definition to tensors, which results in a max-quasi-norm (max-qnorm)
whose unit ball has small Rademacher complexity. We prove that solving a
constrained least squares estimation using either the convex atomic-norm or the
nonconvex max-qnorm results in optimal sample complexity for the problem of
low-rank tensor completion. Furthermore, we show that these bounds are nearly
minimax rate-optimal. We also provide promising numerical results for max-qnorm
constrained tensor completion, showing improved recovery results compared to
matricization and alternating least squares.
Sparse High-Dimensional Linear Regression. Algorithmic Barriers and a Local Search Algorithm
Authors: David Gamarnik, Ilias Zadik
Date: 2017-11-14 00:00:00
Tasks: Denoising, regression

We consider a sparse high dimensional regression model where the goal is to recover a $k$-sparse unknown vector $\beta^*$ from $n$ noisy linear observations of the form $Y=X\beta^*+W \in \mathbb{R}^n$ where $X \in \mathbb{R}^{n \times p}$ has iid $N(0,1)$ entries and $W \in \mathbb{R}^n$ has iid $N(0,\sigma^2)$ entries. Under certain assumptions on the parameters, an intriguing assymptotic gap appears between the minimum value of $n$, call it $n^*$, for which the recovery is information theoretically possible, and the minimum value of $n$, call it $n_{\mathrm{alg}}$, for which an efficient algorithm is known to provably recover $\beta^*$. In \cite{gamarnikzadik} it was conjectured that the gap is not artificial, in the sense that for sample sizes $n \in [n^*,n_{\mathrm{alg}}]$ the problem is algorithmically hard. We support this conjecture in two ways. Firstly, we show that the optimal solution of the LASSO provably fails to $\ell_2$-stably recover the unknown vector $\beta^*$ when $n \in [n^*,c n_{\mathrm{alg}}]$, for some sufficiently small constant $c>0$. Secondly, we establish that $n_{\mathrm{alg}}$, up to a multiplicative constant factor, is a phase transition point for the appearance of a certain Overlap Gap Property (OGP) over the space of $k$-sparse vectors. The presence of such an Overlap Gap Property phase transition, which originates in statistical physics, is known to provide evidence of an algorithmic hardness. Finally we show that if $n>C n_{\mathrm{alg}}$ for some large enough constant $C>0$, a very simple algorithm based on a local search improvement rule is able both to $\ell_2$-stably recover the unknown vector $\beta^*$ and to infer correctly its support, adding it to the list of provably successful algorithms for the high dimensional linear regression problem.
From Word Segmentation to POS Tagging for Vietnamese
Authors: Dat Quoc Nguyen, Thanh Vu, Dai Quoc Nguyen, Mark Dras, Mark Johnson
Date: 2017-11-14 00:00:00
Proceeding: ALTA 2017 12
Tasks: Part-Of-Speech Tagging, POS, POS Tagging, TAG

This paper presents an empirical comparison of two strategies for Vietnamese
Part-of-Speech (POS) tagging from unsegmented text: (i) a pipeline strategy
where we consider the output of a word segmenter as the input of a POS tagger,
and (ii) a joint strategy where we predict a combined segmentation and POS tag
for each syllable. We also make a comparison between state-of-the-art (SOTA)
feature-based and neural network-based models. On the benchmark Vietnamese
treebank (Nguyen et al., 2009), experimental results show that the pipeline
strategy produces better scores of POS tagging from unsegmented text than the
joint strategy, and the highest accuracy is obtained by using a feature-based
model.
Capturing Localized Image Artifacts through a CNN-based Hyper-image Representation
Authors: Parag Shridhar Chandakkar, Baoxin Li
Date: 2017-11-14 00:00:00
Tasks: Image Quality Estimation, Small Data Image Classification

Training deep CNNs to capture localized image artifacts on a relatively small
dataset is a challenging task. With enough images at hand, one can hope that a
deep CNN characterizes localized artifacts over the entire data and their
effect on the output. However, on smaller datasets, such deep CNNs may overfit
and shallow ones find it hard to capture local artifacts. Thus some image-based
small-data applications first train their framework on a collection of patches
(instead of the entire image) to better learn the representation of localized
artifacts. Then the output is obtained by averaging the patch-level results.
Such an approach ignores the spatial correlation among patches and how various
patch locations affect the output. It also fails in cases where few patches
mainly contribute to the image label. To combat these scenarios, we develop the
notion of hyper-image representations. Our CNN has two stages. The first stage
is trained on patches. The second stage utilizes the last layer representation
developed in the first stage to form a hyper-image, which is used to train the
second stage. We show that this approach is able to develop a better mapping
between the image and its output. We analyze additional properties of our
approach and show its effectiveness on one synthetic and two real-world vision
tasks - no-reference image quality estimation and image tampering detection -
by its performance improvement over existing strong baselines.
Parallel Streaming Wasserstein Barycenters
Authors: Matthew Staib, Sebastian Claici, Justin Solomon, Stefanie Jegelka
Date: 2017-05-21 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Bayesian Inference

Efficiently aggregating data from different sources is a challenging problem,
particularly when samples from each source are distributed differently. These
differences can be inherent to the inference task or present for other reasons:
sensors in a sensor network may be placed far apart, affecting their individual
measurements. Conversely, it is computationally advantageous to split Bayesian
inference tasks across subsets of data, but data need not be identically
distributed across subsets. One principled way to fuse probability
distributions is via the lens of optimal transport: the Wasserstein barycenter
is a single distribution that summarizes a collection of input measures while
respecting their geometry. However, computing the barycenter scales poorly and
requires discretization of all input distributions and the barycenter itself.
Improving on this situation, we present a scalable, communication-efficient,
parallel algorithm for computing the Wasserstein barycenter of arbitrary
distributions. Our algorithm can operate directly on continuous input
distributions and is optimized for streaming data. Our method is even robust to
nonstationary input distributions and produces a barycenter estimate that
tracks the input measures over time. The algorithm is semi-discrete, needing to
discretize only the barycenter estimate. To the best of our knowledge, we also
provide the first bounds on the quality of the approximate barycenter as the
discretization becomes finer. Finally, we demonstrate the practical
effectiveness of our method, both in tracking moving distributions on a sphere,
as well as in a large-scale Bayesian inference task.
Deep Learning: A Bayesian Perspective
Authors: Nicholas Polson, Vadim Sokolov
Date: 2017-06-01 00:00:00
Tasks: Deep Learning, regression, Variable Selection

Deep learning is a form of machine learning for nonlinear high dimensional
pattern matching and prediction. By taking a Bayesian probabilistic
perspective, we provide a number of insights into more efficient algorithms for
optimisation and hyper-parameter tuning. Traditional high-dimensional data
reduction techniques, such as principal component analysis (PCA), partial least
squares (PLS), reduced rank regression (RRR), projection pursuit regression
(PPR) are all shown to be shallow learners. Their deep learning counterparts
exploit multiple deep layers of data reduction which provide predictive
performance gains. Stochastic gradient descent (SGD) training optimisation and
Dropout (DO) regularization provide estimation and variable selection. Bayesian
regularization is central to finding weights and connections in networks to
optimize the predictive bias-variance trade-off. To illustrate our methodology,
we provide an analysis of international bookings on Airbnb. Finally, we
conclude with directions for future research.
pyLEMMINGS: Large Margin Multiple Instance Classification and Ranking for Bioinformatics Applications
Authors: Amina Asif, Wajid Arshad Abbasi, Farzeen Munir, Asa Ben-Hur, Fayyaz Ul Amir Afsar Minhas
Date: 2017-11-14 00:00:00
Tasks: General Classification, Multiple Instance Learning

Motivation: A major challenge in the development of machine learning based
methods in computational biology is that data may not be accurately labeled due
to the time and resources required for experimentally annotating properties of
proteins and DNA sequences. Standard supervised learning algorithms assume
accurate instance-level labeling of training data. Multiple instance learning
is a paradigm for handling such labeling ambiguities. However, the widely used
large-margin classification methods for multiple instance learning are
heuristic in nature with high computational requirements. In this paper, we
present stochastic sub-gradient optimization large margin algorithms for
multiple instance classification and ranking, and provide them in a software
suite called pyLEMMINGS.
  Results: We have tested pyLEMMINGS on a number of bioinformatics problems as
well as benchmark datasets. pyLEMMINGS has successfully been able to identify
functionally important segments of proteins: binding sites in Calmodulin
binding proteins, prion forming regions, and amyloid cores. pyLEMMINGS achieves
state-of-the-art performance in all these tasks, demonstrating the value of
multiple instance learning. Furthermore, our method has shown more than
100-fold improvement in terms of running time as compared to heuristic
solutions with improved accuracy over benchmark datasets.
  Availability and Implementation: pyLEMMINGS python package is available for
download at: http://faculty.pieas.edu.pk/fayyaz/software.html#pylemmings.
How we can control the crack to propagate along the specified path feasibly?
Authors: Zhenxing Cheng, Hu Wang
Date: 2017-10-30 00:00:00

A controllable crack propagation (CCP) strategy is suggested. It is well
known that crack always leads the failure by crossing the critical domain in
engineering structure. Therefore, the CCP method is proposed to control the
crack to propagate along the specified path, which is away from the critical
domain. To complete this strategy, two optimization methods are engaged.
Firstly, a back propagation neural network (BPNN) assisted particle swarm
optimization (PSO) is suggested. In this method, to improve the efficiency of
CCP, the BPNN is used to build the metamodel instead of the forward evaluation.
Secondly, the popular PSO is used. Considering the optimization iteration is a
time consuming process, an efficient reanalysis based extended finite element
methods (X-FEM) is used to substitute the complete X-FEM solver to calculate
the crack propagation path. Moreover, an adaptive subdomain partition strategy
is suggested to improve the fitting accuracy between real crack and specified
paths. Several typical numerical examples demonstrate that both optimization
methods can carry out the CCP. The selection of them should be determined by
the tradeoff between efficiency and accuracy.
Sobolev GAN
Authors: Youssef Mroueh, Chun-Liang Li, Tom Sercu, Anant Raj, Yu Cheng
Date: 2017-11-14 00:00:00
Proceeding: ICLR 2018 1
Tasks: Text Generation

We propose a new Integral Probability Metric (IPM) between distributions: the
Sobolev IPM. The Sobolev IPM compares the mean discrepancy of two distributions
for functions (critic) restricted to a Sobolev ball defined with respect to a
dominant measure $\mu$. We show that the Sobolev IPM compares two distributions
in high dimensions based on weighted conditional Cumulative Distribution
Functions (CDF) of each coordinate on a leave one out basis. The Dominant
measure $\mu$ plays a crucial role as it defines the support on which
conditional CDFs are compared. Sobolev IPM can be seen as an extension of the
one dimensional Von-Mises Cram\'er statistics to high dimensional
distributions. We show how Sobolev IPM can be used to train Generative
Adversarial Networks (GANs). We then exploit the intrinsic conditioning implied
by Sobolev IPM in text generation. Finally we show that a variant of Sobolev
GAN achieves competitive results in semi-supervised learning on CIFAR-10,
thanks to the smoothness enforced on the critic by Sobolev GAN which relates to
Laplacian regularization.
STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery
Authors: Mohsen Ghassemi, Zahra Shakeri, Anand D. Sarwate, Waheed U. Bajwa
Date: 2017-11-13 00:00:00
Tasks: Dictionary Learning

In recent years, a class of dictionaries have been proposed for
multidimensional (tensor) data representation that exploit the structure of
tensor data by imposing a Kronecker structure on the dictionary underlying the
data. In this work, a novel algorithm called "STARK" is provided to learn
Kronecker structured dictionaries that can represent tensors of any order. By
establishing that the Kronecker product of any number of matrices can be
rearranged to form a rank-1 tensor, we show that Kronecker structure can be
enforced on the dictionary by solving a rank-1 tensor recovery problem. Because
rank-1 tensor recovery is a challenging nonconvex problem, we resort to solving
a convex relaxation of this problem. Empirical experiments on synthetic and
real data show promising results for our proposed algorithm.
Determinants of Mobile Money Adoption in Pakistan
Authors: Muhammad Raza Khan, Joshua Blumenstock
Date: 2017-11-13 00:00:00

In this work, we analyze the problem of adoption of mobile money in Pakistan
by using the call detail records of a major telecom company as our input. Our
results highlight the fact that different sections of the society have
different patterns of adoption of digital financial services but user mobility
related features are the most important one when it comes to adopting and using
mobile money services.
Accelerating HPC codes on Intel(R) Omni-Path Architecture networks: From particle physics to Machine Learning
Authors: Peter Boyle, Michael Chuvelev, Guido Cossu, Christopher Kelly, Christoph Lehner, Lawrence Meadows
Date: 2017-11-13 00:00:00
Tasks: BIG-bench Machine Learning

We discuss practical methods to ensure near wirespeed performance from
clusters with either one or two Intel(R) Omni-Path host fabric interfaces (HFI)
per node, and Intel(R) Xeon Phi(TM) 72xx (Knight's Landing) processors, and
using the Linux operating system.
  The study evaluates the performance improvements achievable and the required
programming approaches in two distinct example problems: firstly in Cartesian
communicator halo exchange problems, appropriate for structured grid PDE
solvers that arise in quantum chromodynamics simulations of particle physics,
and secondly in gradient reduction appropriate to synchronous stochastic
gradient descent for machine learning. As an example, we accelerate a published
Baidu Research reduction code and obtain a factor of ten speedup over the
original code using the techniques discussed in this paper. This displays how a
factor of ten speedup in strongly scaled distributed machine learning could be
achieved when synchronous stochastic gradient descent is massively parallelised
with a fixed mini-batch size.
  We find a significant improvement in performance robustness when memory is
obtained using carefully allocated 2MB "huge" virtual memory pages, implying
that either non-standard allocation routines should be used for communication
buffers. These can be accessed via a LD\_PRELOAD override in the manner
suggested by libhugetlbfs. We make use of a the Intel(R) MPI 2019 library
"Technology Preview" and underlying software to enable thread concurrency
throughout the communication software stake via multiple PSM2 endpoints per
process and use of multiple independent MPI communicators. When using a single
MPI process per node, we find that this greatly accelerates delivered bandwidth
in many core Intel(R) Xeon Phi processors.
Active Learning for Cost-Sensitive Classification
Authors: Akshay Krishnamurthy, Alekh Agarwal, Tzu-Kuo Huang, Hal Daume III, John Langford
Date: 2017-03-03 00:00:00
Proceeding: ICML 2017 8
Tasks: Active Learning, Classification, General Classification, regression

We design an active learning algorithm for cost-sensitive multiclass classification: problems where different errors have different costs. Our algorithm, COAL, makes predictions by regressing to each label's cost and predicting the smallest. On a new example, it uses a set of regressors that perform well on past data to estimate possible costs for each label. It queries only the labels that could be the best, ignoring the sure losers. We prove COAL can be efficiently implemented for any regression family that admits squared loss optimization; it also enjoys strong guarantees with respect to predictive performance and labeling effort. We empirically compare COAL to passive learning and several active learning baselines, showing significant improvements in labeling effort and test cost on real-world datasets.
Using Phone Sensors and an Artificial Neural Network to Detect Gait Changes During Drinking Episodes in the Natural Environment
Authors: Brian Suffoletto, Pedram Gharani, Tammy Chung, Hassan Karimi
Date: 2017-11-09 00:00:00

Phone sensors could be useful in assessing changes in gait that occur with
alcohol consumption. This study determined (1) feasibility of collecting
gait-related data during drinking occasions in the natural environment, and (2)
how gait-related features measured by phone sensors relate to estimated blood
alcohol concentration (eBAC). Ten young adult heavy drinkers were prompted to
complete a 5-step gait task every hour from 8pm to 12am over four consecutive
weekends. We collected 3-xis accelerometer, gyroscope, and magnetometer data
from phone sensors, and computed 24 gait-related features using a sliding
window technique. eBAC levels were calculated at each time point based on
Ecological Momentary Assessment (EMA) of alcohol use. We used an artificial
neural network model to analyze associations between sensor features and eBACs
in training (70% of the data) and validation and test (30% of the data)
datasets. We analyzed 128 data points where both eBAC and gait-related sensor
data was captured, either when not drinking (n=60), while eBAC was ascending
(n=55) or eBAC was descending (n=13). 21 data points were captured at times
when the eBAC was greater than the legal limit (0.08 mg/dl). Using a Bayesian
regularized neural network, gait-related phone sensor features showed a high
correlation with eBAC (Pearson's r > 0.9), and >95% of estimated eBAC would
fall between -0.012 and +0.012 of actual eBAC. It is feasible to collect
gait-related data from smartphone sensors during drinking occasions in the
natural environment. Sensor-based features can be used to infer gait changes
associated with elevated blood alcohol content.
Modeling Human Categorization of Natural Images Using Deep Feature Representations
Authors: Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
Date: 2017-11-13 00:00:00

Over the last few decades, psychologists have developed sophisticated formal
models of human categorization using simple artificial stimuli. In this paper,
we use modern machine learning methods to extend this work into the realm of
naturalistic stimuli, enabling human categorization to be studied over the
complex visual domain in which it evolved and developed. We show that
representations derived from a convolutional neural network can be used to
model behavior over a database of >300,000 human natural image classifications,
and find that a group of models based on these representations perform well,
near the reliability of human judgments. Interestingly, this group includes
both exemplar and prototype models, contrasting with the dominance of exemplar
models in previous work. We are able to improve the performance of the
remaining models by preprocessing neural network representations to more
closely capture human similarity judgments.
Discovering Potential Correlations via Hypercontractivity
Authors: Hyeji Kim, Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Date: 2017-09-12 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Time Series, Time Series Analysis, Two-sample testing

Discovering a correlation from one variable to another variable is of
fundamental scientific and practical interest. While existing correlation
measures are suitable for discovering average correlation, they fail to
discover hidden or potential correlations. To bridge this gap, (i) we postulate
a set of natural axioms that we expect a measure of potential correlation to
satisfy; (ii) we show that the rate of information bottleneck, i.e., the
hypercontractivity coefficient, satisfies all the proposed axioms; (iii) we
provide a novel estimator to estimate the hypercontractivity coefficient from
samples; and (iv) we provide numerical experiments demonstrating that this
proposed estimator discovers potential correlations among various indicators of
WHO datasets, is robust in discovering gene interactions from gene expression
time series data, and is statistically more powerful than the estimators for
other correlation measures in binary hypothesis testing of canonical examples
of potential correlations.