pwc-archive/papers-with-abstracts huggingface dataset:
On modeling vagueness and uncertainty in data-to-text systems through fuzzy sets
Authors: A. Ramos-Soto, M. Pereira-Fariña
Date: 2017-10-27 00:00:00
Tasks: Management, Text Generation

Vagueness and uncertainty management is counted among one of the challenges
that remain unresolved in systems that generate texts from non-linguistic data,
known as data-to-text systems. In the last decade, work in fuzzy linguistic
summarization and description of data has raised the interest of using fuzzy
sets to model and manage the imprecision of human language in data-to-text
systems. However, despite some research in this direction, there has not been
an actual clear discussion and justification on how fuzzy sets can contribute
to data-to-text for modeling vagueness and uncertainty in words and
expressions. This paper intends to bridge this gap by answering the following
questions: What does vagueness mean in fuzzy sets theory? What does vagueness
mean in data-to-text contexts? In what ways can fuzzy sets theory contribute to
improve data-to-text systems? What are the challenges that researchers from
both disciplines need to address for a successful integration of fuzzy sets
into data-to-text systems? In what cases should the use of fuzzy sets be
avoided in D2T? For this, we review and discuss the state of the art of
vagueness modeling in natural language generation and data-to-text, describe
potential and actual usages of fuzzy sets in data-to-text contexts, and provide
some additional insights about the engineering of data-to-text systems that
make use of fuzzy set-based techniques.
Transform-Invariant Non-Parametric Clustering of Covariance Matrices and its Application to Unsupervised Joint Segmentation and Action Discovery
Authors: Nadia Figueroa, Aude Billard
Date: 2017-10-27 00:00:00
Tasks: Clustering, Time Series, Time Series Analysis

In this work, we tackle the problem of transform-invariant unsupervised
learning in the space of Covariance matrices and applications thereof. We begin
by introducing the Spectral Polytope Covariance Matrix (SPCM) Similarity
function; a similarity function for Covariance matrices, invariant to any type
of transformation. We then derive the SPCM-CRP mixture model, a
transform-invariant non-parametric clustering approach for Covariance matrices
that leverages the proposed similarity function, spectral embedding and the
distance-dependent Chinese Restaurant Process (dd-CRP) (Blei and Frazier,
2011). The scalability and applicability of these two contributions is
extensively validated on real-world Covariance matrix datasets from diverse
research fields. Finally, we couple the SPCM-CRP mixture model with the
Bayesian non-parametric Indian Buffet Process (IBP) - Hidden Markov Model (HMM)
(Fox et al., 2009), to jointly segment and discover transform-invariant action
primitives from complex sequential data. Resulting in a topic-modeling inspired
hierarchical model for unsupervised time-series data analysis which we call
ICSC-HMM (IBP Coupled SPCM-CRP Hidden Markov Model). The ICSC-HMM is validated
on kinesthetic demonstrations of uni-manual and bi-manual cooking tasks;
achieving unsupervised human-level decomposition of complex sequential tasks.
Direction of arrival estimation for multiple sound sources using convolutional recurrent neural network
Authors: Sharath Adavanne, Archontis Politis, Tuomas Virtanen
Date: 2017-10-27 00:00:00
Tasks: Direction of Arrival Estimation

This paper proposes a deep neural network for estimating the directions of
arrival (DOA) of multiple sound sources. The proposed stacked convolutional and
recurrent neural network (DOAnet) generates a spatial pseudo-spectrum (SPS)
along with the DOA estimates in both azimuth and elevation. We avoid any
explicit feature extraction step by using the magnitudes and phases of the
spectrograms of all the channels as input to the network. The proposed DOAnet
is evaluated by estimating the DOAs of multiple concurrently present sources in
anechoic, matched and unmatched reverberant conditions. The results show that
the proposed DOAnet is capable of estimating the number of sources and their
respective DOAs with good precision and generate SPS with high signal-to-noise
ratio.
Multiwinner Voting with Fairness Constraints
Authors: L. Elisa Celis, Lingxiao Huang, Nisheeth K. Vishnoi
Date: 2017-10-27 00:00:00
Tasks: Attribute, Fairness

Multiwinner voting rules are used to select a small representative subset of
candidates or items from a larger set given the preferences of voters. However,
if candidates have sensitive attributes such as gender or ethnicity (when
selecting a committee), or specified types such as political leaning (when
selecting a subset of news items), an algorithm that chooses a subset by
optimizing a multiwinner voting rule may be unbalanced in its selection -- it
may under or over represent a particular gender or political orientation in the
examples above. We introduce an algorithmic framework for multiwinner voting
problems when there is an additional requirement that the selected subset
should be "fair" with respect to a given set of attributes. Our framework
provides the flexibility to (1) specify fairness with respect to multiple,
non-disjoint attributes (e.g., ethnicity and gender) and (2) specify a score
function. We study the computational complexity of this constrained multiwinner
voting problem for monotone and submodular score functions and present several
approximation algorithms and matching hardness of approximation results for
various attribute group structure and types of score functions. We also present
simulations that suggest that adding fairness constraints may not affect the
scores significantly when compared to the unconstrained case.
Distributional Reinforcement Learning with Quantile Regression
Authors: Will Dabney, Mark Rowland, Marc G. Bellemare, Rémi Munos
Date: 2017-10-27 00:00:00
Tasks: Atari Games, Distributional Reinforcement Learning, quantile regression, regression, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In reinforcement learning an agent interacts with the environment by taking
actions and observing the next state and reward. When sampled
probabilistically, these state transitions, rewards, and actions can all induce
randomness in the observed long-term return. Traditionally, reinforcement
learning algorithms average over this randomness to estimate the value
function. In this paper, we build on recent work advocating a distributional
approach to reinforcement learning in which the distribution over returns is
modeled explicitly instead of only estimating the mean. That is, we examine
methods of learning the value distribution instead of the value function. We
give results that close a number of gaps between the theoretical and
algorithmic results given by Bellemare, Dabney, and Munos (2017). First, we
extend existing results to the approximate distribution setting. Second, we
present a novel distributional reinforcement learning algorithm consistent with
our theoretical formulation. Finally, we evaluate this new algorithm on the
Atari 2600 games, observing that it significantly outperforms many of the
recent improvements on DQN, including the related distributional algorithm C51.
Convolutional neural networks on irregular domains based on approximate vertex-domain translations
Authors: Bastien Pasdeloup, Vincent Gripon, Jean-Charles Vialatte, Dominique Pastor, Pascal Frossard
Date: 2017-10-27 00:00:00
Tasks: Translation

We propose a generalization of convolutional neural networks (CNNs) to
irregular domains, through the use of a translation operator on a graph
structure. In regular settings such as images, convolutional layers are
designed by translating a convolutional kernel over all pixels, thus enforcing
translation equivariance. In the case of general graphs however, translation is
not a well-defined operation, which makes shifting a convolutional kernel not
straightforward. In this article, we introduce a methodology to allow the
design of convolutional layers that are adapted to signals evolving on
irregular topologies, even in the absence of a natural translation. Using the
designed layers, we build a CNN that we train using the initial set of signals.
Contrary to other approaches that aim at extending CNNs to irregular domains,
we incorporate the classical settings of CNNs for 2D signals as a particular
case of our approach. Designing convolutional layers in the vertex domain
directly implies weight sharing, which in other approaches is generally
estimated a posteriori using heuristics.
Online Learning of Power Transmission Dynamics
Authors: Andrey Y. Lokhov, Marc Vuffray, Dmitry Shemetov, Deepjyoti Deka, Michael Chertkov
Date: 2017-10-27 00:00:00

We consider the problem of reconstructing the dynamic state matrix of
transmission power grids from time-stamped PMU measurements in the regime of
ambient fluctuations. Using a maximum likelihood based approach, we construct a
family of convex estimators that adapt to the structure of the problem
depending on the available prior information. The proposed method is fully
data-driven and does not assume any knowledge of system parameters. It can be
implemented in near real-time and requires a small amount of data. Our learning
algorithms can be used for model validation and calibration, and can also be
applied to related problems of system stability, detection of forced
oscillations, generation re-dispatch, as well as to the estimation of the
system state.
Regularization via Mass Transportation
Authors: Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani
Date: 2017-10-27 00:00:00
Tasks: Generalization Bounds

The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, overfitting is typically mitigated by adding regularization terms to the objective that penalize hypothesis complexity. In this paper we introduce new regularization techniques using ideas from distributionally robust optimization, and we give new probabilistic interpretations to existing techniques. Specifically, we propose to minimize the worst-case expected loss, where the worst case is taken over the ball of all (continuous or discrete) distributions that have a bounded transportation distance from the (discrete) empirical distribution. By choosing the radius of this ball judiciously, we can guarantee that the worst-case expected loss provides an upper confidence bound on the loss on test data, thus offering new generalization bounds. We prove that the resulting regularized learning problems are tractable and can be tractably kernelized for many popular loss functions. We validate our theoretical out-of-sample guarantees through simulated and empirical experiments.
SGDLibrary: A MATLAB library for stochastic gradient descent algorithms
Authors: Hiroyuki Kasai
Date: 2017-10-27 00:00:00
Tasks: Stochastic Optimization

We consider the problem of finding the minimizer of a function $f:
\mathbb{R}^d \rightarrow \mathbb{R}$ of the finite-sum form $\min f(w) =
1/n\sum_{i}^n f_i(w)$. This problem has been studied intensively in recent
years in the field of machine learning (ML). One promising approach for
large-scale data is to use a stochastic optimization algorithm to solve the
problem. SGDLibrary is a readable, flexible and extensible pure-MATLAB library
of a collection of stochastic optimization algorithms. The purpose of the
library is to provide researchers and implementers a comprehensive evaluation
environment for the use of these algorithms on various ML problems.
Deep Learning for Accelerated Ultrasound Imaging
Authors: Yeo Hun Yoon, Jong Chul Ye
Date: 2017-10-27 00:00:00
Tasks: Deep Learning

In portable, 3-D, or ultra-fast ultrasound (US) imaging systems, there is an
increasing demand to reconstruct high quality images from limited number of
data. However, the existing solutions require either hardware changes or
computationally expansive algorithms. To overcome these limitations, here we
propose a novel deep learning approach that interpolates the missing RF data by
utilizing the sparsity of the RF data in the Fourier domain. Extensive
experimental results from sub-sampled RF data from a real US system confirmed
that the proposed method can effectively reduce the data rate without
sacrificing the image quality.
Deterministic Approximate Methods for Maximum Consensus Robust Fitting
Authors: Huu Le, Tat-Jun Chin, Anders Eriksson, Thanh-Toan Do, David Suter
Date: 2017-10-27 00:00:00

Maximum consensus estimation plays a critically important role in robust
fitting problems in computer vision. Currently, the most prevalent algorithms
for consensus maximization draw from the class of randomized
hypothesize-and-verify algorithms, which are cheap but can usually deliver only
rough approximate solutions. On the other extreme, there are exact algorithms
which are exhaustive search in nature and can be costly for practical-sized
inputs. This paper fills the gap between the two extremes by proposing
deterministic algorithms to approximately optimize the maximum consensus
criterion. Our work begins by reformulating consensus maximization with linear
complementarity constraints. Then, we develop two novel algorithms: one based
on non-smooth penalty method with a Frank-Wolfe style optimization scheme, the
other based on the Alternating Direction Method of Multipliers (ADMM). Both
algorithms solve convex subproblems to efficiently perform the optimization. We
demonstrate the capability of our algorithms to greatly improve a rough initial
estimate, such as those obtained using least squares or a randomized algorithm.
Compared to the exact algorithms, our approach is much more practical on
realistic input sizes. Further, our approach is naturally applicable to
estimation problems with geometric residuals
Online linear optimization with the log-determinant regularizer
Authors: Ken-ichiro Moridomi, Kohei Hatano, Eiji Takimoto
Date: 2017-10-27 00:00:00
Tasks: Collaborative Filtering

We consider online linear optimization over symmetric positive semi-definite
matrices, which has various applications including the online collaborative
filtering. The problem is formulated as a repeated game between the algorithm
and the adversary, where in each round t the algorithm and the adversary choose
matrices X_t and L_t, respectively, and then the algorithm suffers a loss given
by the Frobenius inner product of X_t and L_t. The goal of the algorithm is to
minimize the cumulative loss. We can employ a standard framework called Follow
the Regularized Leader (FTRL) for designing algorithms, where we need to choose
an appropriate regularization function to obtain a good performance guarantee.
We show that the log-determinant regularization works better than other popular
regularization functions in the case where the loss matrices L_t are all
sparse. Using this property, we show that our algorithm achieves an optimal
performance guarantee for the online collaborative filtering. The technical
contribution of the paper is to develop a new technique of deriving performance
bounds by exploiting the property of strong convexity of the log-determinant
with respect to the loss matrices, while in the previous analysis the strong
convexity is defined with respect to a norm. Intuitively, skipping the norm
analysis results in the improved bound. Moreover, we apply our method to online
linear optimization over vectors and show that the FTRL with the Burg entropy
regularizer, which is the analogue of the log-determinant regularizer in the
vector case, works well.
Easily parallelizable and distributable class of algorithms for structured sparsity, with optimal acceleration
Authors: Seyoon Ko, Donghyeon Yu, Joong-Ho Won
Date: 2017-02-21 00:00:00
Tasks: Distributed Computing

Many statistical learning problems can be posed as minimization of a sum of
two convex functions, one typically a composition of non-smooth and linear
functions. Examples include regression under structured sparsity assumptions.
Popular algorithms for solving such problems, e.g., ADMM, often involve
non-trivial optimization subproblems or smoothing approximation. We consider
two classes of primal-dual algorithms that do not incur these difficulties, and
unify them from a perspective of monotone operator theory. From this
unification we propose a continuum of preconditioned forward-backward operator
splitting algorithms amenable to parallel and distributed computing. For the
entire region of convergence of the whole continuum of algorithms, we establish
its rates of convergence. For some known instances of this continuum, our
analysis closes the gap in theory. We further exploit the unification to
propose a continuum of accelerated algorithms. We show that the whole continuum
attains the theoretically optimal rate of convergence. The scalability of the
proposed algorithms, as well as their convergence behavior, is demonstrated up
to 1.2 million variables with a distributed implementation.
Class Correlation affects Single Object Localization using Pre-trained ConvNets
Authors: Pokkalla Harsha Vardhan, Kunal Sekhri, Dipan K. Pal, Marios Savvides
Date: 2017-10-26 00:00:00
Tasks: Object, Object Localization

The problem of object localization has become one of the mainstream problems
of vision. Most of the algorithms proposed involve the design for the model to
be specifically for localizing objects. In this paper, we explore whether a
pre-trained canonical ConvNet (without fine-tuning) trained purely for object
classification on one dataset with global image level labels can be used to
localize objects in images containing a single instance on a separate dataset
while generalizing to novel classes. We propose a simple algorithm involving
cropping and blackening out regions in the image space called Explicit Image
Space based Search (EISS) for locating the most responsive regions in an image
in the context of object localization. EISS brings to light the interesting
phenomenon of a ConvNets responding more to features within objects as opposed
to object level descriptors, as the classes in the training data get more
correlated (visually/semantically similar).
Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes
Authors: Aaron Sidford, Mengdi Wang, Xian Wu, Yinyu Ye
Date: 2017-10-27 00:00:00

In this paper we provide faster algorithms for approximately solving discounted Markov Decision Processes in multiple parameter regimes. Given a discounted Markov Decision Process (DMDP) with $|S|$ states, $|A|$ actions, discount factor $\gamma\in(0,1)$, and rewards in the range $[-M, M]$, we show how to compute an $\epsilon$-optimal policy, with probability $1 - \delta$ in time \[ \tilde{O}\left( \left(|S|^2 |A| + \frac{|S| |A|}{(1 - \gamma)^3} \right) \log\left( \frac{M}{\epsilon} \right) \log\left( \frac{1}{\delta} \right) \right) ~ . \] This contribution reflects the first nearly linear time, nearly linearly convergent algorithm for solving DMDPs for intermediate values of $\gamma$. We also show how to obtain improved sublinear time algorithms provided we can sample from the transition function in $O(1)$ time. Under this assumption we provide an algorithm which computes an $\epsilon$-optimal policy with probability $1 - \delta$ in time \[ \tilde{O} \left(\frac{|S| |A| M^2}{(1 - \gamma)^4 \epsilon^2} \log \left(\frac{1}{\delta}\right) \right) ~. \] Lastly, we extend both these algorithms to solve finite horizon MDPs. Our algorithms improve upon the previous best for approximately computing optimal policies for fixed-horizon MDPs in multiple parameter regimes. Interestingly, we obtain our results by a careful modification of approximate value iteration. We show how to combine classic approximate value iteration analysis with new techniques in variance reduction. Our fastest algorithms leverage further insights to ensure that our algorithms make monotonic progress towards the optimal value. This paper is one of few instances in using sampling to obtain a linearly convergent linear programming algorithm and we hope that the analysis may be useful more broadly.
Stochastic Conjugate Gradient Algorithm with Variance Reduction
Authors: Xiao-Bo Jin, Xu-Yao Zhang, Kai-Zhu Huang, Guang-Gang Geng
Date: 2017-10-27 00:00:00
Tasks: Computational Efficiency

Conjugate gradient (CG) methods are a class of important methods for solving
linear equations and nonlinear optimization problems. In this paper, we propose
a new stochastic CG algorithm with variance reduction and we prove its linear
convergence with the Fletcher and Reeves method for strongly convex and smooth
functions. We experimentally demonstrate that the CG with variance reduction
algorithm converges faster than its counterparts for four learning models,
which may be convex, nonconvex or nonsmooth. In addition, its area under the
curve performance on six large-scale data sets is comparable to that of the
LIBLINEAR solver for the L2-regularized L2-loss but with a significant
improvement in computational efficiency
Distributed Asynchronous Dual Free Stochastic Dual Coordinate Ascent
Authors: Zhouyuan Huo, Heng Huang
Date: 2016-05-29 00:00:00
Tasks: Distributed Optimization

The primal-dual distributed optimization methods have broad large-scale
machine learning applications. Previous primal-dual distributed methods are not
applicable when the dual formulation is not available, e.g. the
sum-of-non-convex objectives. Moreover, these algorithms and theoretical
analysis are based on the fundamental assumption that the computing speeds of
multiple machines in a cluster are similar. However, the straggler problem is
an unavoidable practical issue in the distributed system because of the
existence of slow machines. Therefore, the total computational time of the
distributed optimization methods is highly dependent on the slowest machine. In
this paper, we address these two issues by proposing distributed asynchronous
dual free stochastic dual coordinate ascent algorithm for distributed
optimization. Our method does not need the dual formulation of the target
problem in the optimization. We tackle the straggler problem through
asynchronous communication and the negative effect of slow machines is
significantly alleviated. We also analyze the convergence rate of our method
and prove the linear convergence rate even if the individual functions in
objective are non-convex. Experiments on both convex and non-convex loss
functions are used to validate our statements.
Sound Source Localization in a Multipath Environment Using Convolutional Neural Networks
Authors: Eric L. Ferguson, Stefan B. Williams, Craig T. Jin
Date: 2017-10-27 00:00:00
Tasks: Sound Source Localization

The propagation of sound in a shallow water environment is characterized by
boundary reflections from the sea surface and sea floor. These reflections
result in multiple (indirect) sound propagation paths, which can degrade the
performance of passive sound source localization methods. This paper proposes
the use of convolutional neural networks (CNNs) for the localization of sources
of broadband acoustic radiated noise (such as motor vessels) in shallow water
multipath environments. It is shown that CNNs operating on cepstrogram and
generalized cross-correlogram inputs are able to more reliably estimate the
instantaneous range and bearing of transiting motor vessels when the source
localization performance of conventional passive ranging methods is degraded.
The ensuing improvement in source localization performance is demonstrated
using real data collected during an at-sea experiment.