pwc-archive/papers-with-abstracts huggingface dataset:
Smooth Imitation Learning for Online Sequence Prediction
Authors: Hoang M. Le, Andrew Kang, Yisong Yue, Peter Carr
Date: 2016-06-03 00:00:00
Tasks: Imitation Learning, Prediction, regression

We study the problem of smooth imitation learning for online sequence
prediction, where the goal is to train a policy that can smoothly imitate
demonstrated behavior in a dynamic and continuous environment in response to
online, sequential context input. Since the mapping from context to behavior is
often complex, we take a learning reduction approach to reduce smooth imitation
learning to a regression problem using complex function classes that are
regularized to ensure smoothness. We present a learning meta-algorithm that
achieves fast and stable convergence to a good policy. Our approach enjoys
several attractive properties, including being fully deterministic, employing
an adaptive learning rate that can provably yield larger policy improvements
compared to previous approaches, and the ability to ensure stable convergence.
Our empirical results demonstrate significant performance gains over previous
approaches.
L1-Regularized Distributed Optimization: A Communication-Efficient Primal-Dual Framework
Authors: Virginia Smith, Simone Forte, Michael. I. Jordan, Martin Jaggi
Date: 2015-12-13 00:00:00
Tasks: Distributed Optimization

Despite the importance of sparsity in many large-scale applications, there
are few methods for distributed optimization of sparsity-inducing objectives.
In this paper, we present a communication-efficient framework for
L1-regularized optimization in the distributed environment. By viewing
classical objectives in a more general primal-dual setting, we develop a new
class of methods that can be efficiently distributed and applied to common
sparsity-inducing models, such as Lasso, sparse logistic regression, and
elastic net-regularized problems. We provide theoretical convergence guarantees
for our framework, and demonstrate its efficiency and flexibility with a
thorough experimental comparison on Amazon EC2. Our proposed framework yields
speedups of up to 50x as compared to current state-of-the-art methods for
distributed L1-regularized optimization.
Comparison of 14 different families of classification algorithms on 115 binary datasets
Authors: Jacques Wainer
Date: 2016-06-02 00:00:00
Tasks: General Classification, Quantization

We tested 14 very different classification algorithms (random forest,
gradient boosting machines, SVM - linear, polynomial, and RBF - 1-hidden-layer
neural nets, extreme learning machines, k-nearest neighbors and a bagging of
knn, naive Bayes, learning vector quantization, elastic net logistic
regression, sparse linear discriminant analysis, and a boosting of linear
classifiers) on 115 real life binary datasets. We followed the Demsar analysis
and found that the three best classifiers (random forest, gbm and RBF SVM) are
not significantly different from each other. We also discuss that a change of
less then 0.0112 in the error rate should be considered as an irrelevant
change, and used a Bayesian ANOVA analysis to conclude that with high
probability the differences between these three classifiers is not of practical
consequence. We also verified the execution time of "standard implementations"
of these algorithms and concluded that RBF SVM is the fastest (significantly
so) both in training time and in training plus testing time.
An interactive fuzzy goal programming algorithm to solve decentralized bi-level multiobjective fractional programming problem
Authors: Hasan Dalman
Date: 2016-06-02 00:00:00

This paper proposes a fuzzy goal programming based on Taylor series for
solving decentralized bi-level multiobjective fractional programming (DBLMOFP)
problem. In the proposed approach, all of the membership functions are
associated with the fuzzy goals of each objective at the both levels and also
the fractional membership functions are converted to linear functions using the
Taylor series approach. Then a fuzzy goal programming is proposed to reach the
highest degree of each of the membership goals by taking the most satisfactory
solution for all decision makers at the both levels. Finally, a numerical
example is presented to illustrate the effectiveness of the proposed approach.
Towards a Job Title Classification System
Authors: Faizan Javed, Matt McNair, Ferosh Jacob, Meng Zhao
Date: 2016-06-02 00:00:00
Tasks: BIG-bench Machine Learning, Classification, Clustering, Document Classification, General Classification

Document classification for text, images and other applicable entities has
long been a focus of research in academia and also finds application in many
industrial settings. Amidst a plethora of approaches to solve such problems,
machine-learning techniques have found success in a variety of scenarios. In
this paper we discuss the design of a machine learning-based semi-supervised
job title classification system for the online job recruitment domain currently
in production at CareerBuilder.com and propose enhancements to it. The system
leverages a varied collection of classification as well clustering algorithms.
These algorithms are encompassed in an architecture that facilitates leveraging
existing off-the-shelf machine learning tools and techniques while keeping into
consideration the challenges of constructing a scalable classification system
for a large taxonomy of categories. As a continuously evolving system that is
still under development we first discuss the existing semi-supervised
classification system which is composed of both clustering and classification
components in a proximity-based classifier setup and results of which are
already used across numerous products at CareerBuilder. We then elucidate our
long-term goals for job title classification and propose enhancements to the
existing system in the form of a two-stage coarse and fine level classifier
augmentation to construct a cascade of hierarchical vertical classifiers.
Preliminary results are presented using experimental evaluation on real world
industrial data.
Primal-Dual Rates and Certificates
Authors: Celestine Dünner, Simone Forte, Martin Takáč, Martin Jaggi
Date: 2016-02-16 00:00:00
Tasks: BIG-bench Machine Learning

We propose an algorithm-independent framework to equip existing optimization
methods with primal-dual certificates. Such certificates and corresponding rate
of convergence guarantees are important for practitioners to diagnose progress,
in particular in machine learning applications. We obtain new primal-dual
convergence rates, e.g., for the Lasso as well as many L1, Elastic Net, group
Lasso and TV-regularized problems. The theory applies to any norm-regularized
generalized linear model. Our approach provides efficiently computable duality
gaps which are globally defined, without modifying the original problems in the
region of interest.
Unsupervised Image Segmentation using the Deffuant-Weisbuch Model from Social Dynamics
Authors: Subhradeep Kayal
Date: 2016-04-15 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Unsupervised Image Segmentation

Unsupervised image segmentation algorithms aim at identifying disjoint
homogeneous regions in an image, and have been subject to considerable
attention in the machine vision community. In this paper, a popular theoretical
model with it's origins in statistical physics and social dynamics, known as
the Deffuant-Weisbuch model, is applied to the image segmentation problem. The
Deffuant-Weisbuch model has been found to be useful in modelling the evolution
of a closed system of interacting agents characterised by their opinions or
beliefs, leading to the formation of clusters of agents who share a similar
opinion or belief at steady state. In the context of image segmentation, this
paper considers a pixel as an agent and it's colour property as it's opinion,
with opinion updates as per the Deffuant-Weisbuch model. Apart from applying
the basic model to image segmentation, this paper incorporates adjacency and
neighbourhood information in the model, which factors in the local similarity
and smoothness properties of images. Convergence is reached when the number of
unique pixel opinions, i.e., the number of colour centres, matches the
pre-specified number of clusters. Experiments are performed on a set of images
from the Berkeley Image Segmentation Dataset and the results are analysed both
qualitatively and quantitatively, which indicate that this simple and intuitive
method is promising for image segmentation. To the best of the knowledge of the
author, this is the first work where a theoretical model from statistical
physics and social dynamics has been successfully applied to image processing.
Unified Framework for Quantification
Authors: Aykut Firat
Date: 2016-06-02 00:00:00
Tasks: BIG-bench Machine Learning, General Classification, regression

Quantification is the machine learning task of estimating test-data class
proportions that are not necessarily similar to those in training. Apart from
its intrinsic value as an aggregate statistic, quantification output can also
be used to optimize classifier probabilities, thereby increasing classification
accuracy. We unify major quantification approaches under a constrained
multi-variate regression framework, and use mathematical programming to
estimate class proportions for different loss functions. With this modeling
approach, we extend existing binary-only quantification approaches to
multi-class settings as well. We empirically verify our unified framework by
experimenting with several multi-class datasets including the Stanford
Sentiment Treebank and CIFAR-10.
Sequential Principal Curves Analysis
Authors: Valero Laparra, Jesus Malo
Date: 2016-06-02 00:00:00
Tasks: Dimensionality Reduction, Philosophy

This work includes all the technical details of the Sequential Principal
Curves Analysis (SPCA) in a single document. SPCA is an unsupervised nonlinear
and invertible feature extraction technique. The identified curvilinear
features can be interpreted as a set of nonlinear sensors: the response of each
sensor is the projection onto the corresponding feature. Moreover, it can be
easily tuned for different optimization criteria; e.g. infomax, error
minimization, decorrelation; by choosing the right way to measure distances
along each curvilinear feature. Even though proposed in [Laparra et al. Neural
Comp. 12] and shown to work in multiple modalities in [Laparra and Malo
Frontiers Hum. Neuro. 15], the SPCA framework has its original roots in the
nonlinear ICA algorithm in [Malo and Gutierrez Network 06]. Later on, the SPCA
philosophy for nonlinear generalization of PCA originated substantially faster
alternatives at the cost of introducing different constraints in the model.
Namely, the Principal Polynomial Analysis (PPA) [Laparra et al. IJNS 14], and
the Dimensionality Reduction via Regression (DRR) [Laparra et al. IEEE TGRS
15]. This report illustrates the reasons why we developed such family and is
the appropriate technical companion for the missing details in [Laparra et al.,
NeCo 12, Laparra and Malo, Front.Hum.Neuro. 15]. See also the data, code and
examples in the dedicated sites http://isp.uv.es/spca.html and
http://isp.uv.es/after effects.html
High Dimensional Multivariate Regression and Precision Matrix Estimation via Nonconvex Optimization
Authors: Jinghui Chen, Quanquan Gu
Date: 2016-06-02 00:00:00
Tasks: regression, Vocal Bursts Intensity Prediction

We propose a nonconvex estimator for joint multivariate regression and
precision matrix estimation in the high dimensional regime, under sparsity
constraints. A gradient descent algorithm with hard thresholding is developed
to solve the nonconvex estimator, and it attains a linear rate of convergence
to the true regression coefficients and precision matrix simultaneously, up to
the statistical error. Compared with existing methods along this line of
research, which have little theoretical guarantee, the proposed algorithm not
only is computationally much more efficient with provable convergence
guarantee, but also attains the optimal finite sample statistical rate up to a
logarithmic factor. Thorough experiments on both synthetic and real datasets
back up our theory.
Unifying Geometric Features and Facial Action Units for Improved Performance of Facial Expression Analysis
Authors: Mehdi Ghayoumi, Arvind K Bansal
Date: 2016-06-02 00:00:00
Tasks: Dimensionality Reduction, Emotion Recognition, General Classification, image-classification, Image Classification

Previous approaches to model and analyze facial expression analysis use three
different techniques: facial action units, geometric features and graph based
modelling. However, previous approaches have treated these technique
separately. There is an interrelationship between these techniques. The facial
expression analysis is significantly improved by utilizing these mappings
between major geometric features involved in facial expressions and the subset
of facial action units whose presence or absence are unique to a facial
expression. This paper combines dimension reduction techniques and image
classification with search space pruning achieved by this unique subset of
facial action units to significantly prune the search space. The performance
results on the publicly facial expression database shows an improvement in
performance by 70% over time while maintaining the emotion recognition
correctness.
Generalized Root Models: Beyond Pairwise Graphical Models for Univariate Exponential Families
Authors: David I. Inouye, Pradeep Ravikumar, Inderjit S. Dhillon
Date: 2016-06-02 00:00:00

We present a novel k-way high-dimensional graphical model called the
Generalized Root Model (GRM) that explicitly models dependencies between
variable sets of size k > 2---where k = 2 is the standard pairwise graphical
model. This model is based on taking the k-th root of the original sufficient
statistics of any univariate exponential family with positive sufficient
statistics, including the Poisson and exponential distributions. As in the
recent work with square root graphical (SQR) models [Inouye et al.
2016]---which was restricted to pairwise dependencies---we give the conditions
of the parameters that are needed for normalization using the radial
conditionals similar to the pairwise case [Inouye et al. 2016]. In particular,
we show that the Poisson GRM has no restrictions on the parameters and the
exponential GRM only has a restriction akin to negative definiteness. We
develop a simple but general learning algorithm based on L1-regularized
node-wise regressions. We also present a general way of numerically
approximating the log partition function and associated derivatives of the GRM
univariate node conditionals---in contrast to [Inouye et al. 2016], which only
provided algorithm for estimating the exponential SQR. To illustrate GRM, we
model word counts with a Poisson GRM and show the associated k-sized variable
sets. We finish by discussing methods for reducing the parameter space in
various situations.
On the robustness of learning in games with stochastically perturbed payoff observations
Authors: Mario Bravo, Panayotis Mertikopoulos
Date: 2014-12-20 00:00:00

Motivated by the scarcity of accurate payoff feedback in practical
applications of game theory, we examine a class of learning dynamics where
players adjust their choices based on past payoff observations that are subject
to noise and random disturbances. First, in the single-player case
(corresponding to an agent trying to adapt to an arbitrarily changing
environment), we show that the stochastic dynamics under study lead to no
regret almost surely, irrespective of the noise level in the player's
observations. In the multi-player case, we find that dominated strategies
become extinct and we show that strict Nash equilibria are stochastically
stable and attracting; conversely, if a state is stable or attracting with
positive probability, then it is a Nash equilibrium. Finally, we provide an
averaging principle for 2-player games, and we show that in zero-sum games with
an interior equilibrium, time averages converge to Nash equilibrium for any
noise level.
Conditional Dependence via Shannon Capacity: Axioms, Estimators and Applications
Authors: Weihao Gao, Sreeram Kannan, Sewoong Oh, Pramod Viswanath
Date: 2016-02-10 00:00:00

We conduct an axiomatic study of the problem of estimating the strength of a
known causal relationship between a pair of variables. We propose that an
estimate of causal strength should be based on the conditional distribution of
the effect given the cause (and not on the driving distribution of the cause),
and study dependence measures on conditional distributions. Shannon capacity,
appropriately regularized, emerges as a natural measure under these axioms. We
examine the problem of calculating Shannon capacity from the observed samples
and propose a novel fixed-$k$ nearest neighbor estimator, and demonstrate its
consistency. Finally, we demonstrate an application to single-cell
flow-cytometry, where the proposed estimators significantly reduce sample
complexity.
f-GAN: Training Generative Neural Samplers using Variational Divergence Minimization
Authors: Sebastian Nowozin, Botond Cseke, Ryota Tomioka
Date: 2016-06-02 00:00:00
Proceeding: NeurIPS 2016 12

Generative neural samplers are probabilistic models that implement sampling
using feedforward neural networks: they take a random input vector and produce
a sample from a probability distribution defined by the network weights. These
models are expressive and allow efficient computation of samples and
derivatives, but cannot be used for computing likelihoods or for
marginalization. The generative-adversarial training method allows to train
such models through the use of an auxiliary discriminative neural network. We
show that the generative-adversarial approach is a special case of an existing
more general variational divergence estimation approach. We show that any
f-divergence can be used for training generative neural samplers. We discuss
the benefits of various choices of divergence functions on training complexity
and the quality of the obtained generative models.
Deep Motif: Visualizing Genomic Sequence Classifications
Authors: Jack Lanchantin, Ritambhara Singh, Zeming Lin, Yanjun Qi
Date: 2016-05-04 00:00:00

This paper applies a deep convolutional/highway MLP framework to classify
genomic sequences on the transcription factor binding site task. To make the
model understandable, we propose an optimization driven strategy to extract
"motifs", or symbolic patterns which visualize the positive class learned by
the network. We show that our system, Deep Motif (DeMo), extracts motifs that
are similar to, and in some cases outperform the current well known motifs. In
addition, we find that a deeper model consisting of multiple convolutional and
highway layers can outperform a single convolutional and fully connected layer
in the previous state-of-the-art.
Estimacion de carga muscular mediante imagenes
Authors: Leandro Abraham, Facundo Bromberg, Raymundo Forradellas
Date: 2016-05-09 00:00:00

Un problema de gran interes en disciplinas como la ocupacional, ergonomica y
deportiva, es la medicion de variables biomecanicas involucradas en el
movimiento humano (como las fuerzas musculares internas y torque de
articulaciones). Actualmente este problema se resuelve en un proceso de dos
pasos. Primero capturando datos con dispositivos poco pr\'acticos, intrusivos y
costosos. Luego estos datos son usados como entrada en modelos complejos para
obtener las variables biomecanicas como salida. El presente trabajo representa
una alternativa automatizada, no intrusiva y economica al primer paso,
proponiendo la captura de estos datos a traves de imagenes. En trabajos futuros
la idea es automatizar todo el proceso de calculo de esas variables. En este
trabajo elegimos un caso particular de medicion de variables biomecanicas: el
problema de estimar el nivel discreto de carga muscular que estan ejerciendo
los musculos de un brazo. Para estimar a partir de imagenes estaticas del brazo
ejerciendo la fuerza de sostener la carga, el nivel de la misma, realizamos un
proceso de clasificacion. Nuestro enfoque utiliza Support Vector Machines para
clasificacion, combinada con una etapa de pre-procesamiento que extrae
caracter{\i}sticas visuales utilizando variadas tecnicas (Bag of Keypoints,
Local Binary Patterns, Histogramas de Color, Momentos de Contornos) En los
mejores casos (Local Binary Patterns y Momentos de Contornos) obtenemos medidas
de performance en la clasificacion (Precision, Recall, F-Measure y Accuracy)
superiores al 90 %.
On-line Active Reward Learning for Policy Optimisation in Spoken Dialogue Systems
Authors: Pei-Hao Su, Milica Gasic, Nikola Mrksic, Lina Rojas-Barahona, Stefan Ultes, David Vandyke, Tsung-Hsien Wen, Steve Young
Date: 2016-05-24 00:00:00
Proceeding: ACL 2016 8
Tasks: Active Learning, Decoder, Reinforcement Learning, Spoken Dialogue Systems

The ability to compute an accurate reward function is essential for
optimising a dialogue policy via reinforcement learning. In real-world
applications, using explicit user feedback as the reward signal is often
unreliable and costly to collect. This problem can be mitigated if the user's
intent is known in advance or data is available to pre-train a task success
predictor off-line. In practice neither of these apply for most real world
applications. Here we propose an on-line learning framework whereby the
dialogue policy is jointly trained alongside the reward model via active
learning with a Gaussian process model. This Gaussian process operates on a
continuous space dialogue representation generated in an unsupervised fashion
using a recurrent neural network encoder-decoder. The experimental results
demonstrate that the proposed framework is able to significantly reduce data
annotation costs and mitigate noisy user feedback in dialogue policy learning.
Forecasting Framework for Open Access Time Series in Energy
Authors: Gergo Barta, Gabor Nagy, Gabor Simon, Gyozo Papp
Date: 2016-06-02 00:00:00
Tasks: Time Series, Time Series Analysis

In this paper we propose a framework for automated forecasting of
energy-related time series using open access data from European Network of
Transmission System Operators for Electricity (ENTSO-E). The framework provides
forecasts for various European countries using publicly available historical
data only. Our solution was benchmarked using the actual load data and the
country provided estimates (where available). We conclude that the proposed
system can produce timely forecasts with comparable prediction accuracy in a
number of cases. We also investigate the probabilistic case of forecasting -
that is, providing a probability distribution rather than a simple point
forecast - and incorporate it into a web based API that provides quick and easy
access to reliable forecasts.
Death and Suicide in Universal Artificial Intelligence
Authors: Jarryd Martin, Tom Everitt, Marcus Hutter
Date: 2016-06-02 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

Reinforcement learning (RL) is a general paradigm for studying intelligent
behaviour, with applications ranging from artificial intelligence to psychology
and economics. AIXI is a universal solution to the RL problem; it can learn any
computable environment. A technical subtlety of AIXI is that it is defined
using a mixture over semimeasures that need not sum to 1, rather than over
proper probability measures. In this work we argue that the shortfall of a
semimeasure can naturally be interpreted as the agent's estimate of the
probability of its death. We formally define death for generally intelligent
agents like AIXI, and prove a number of related theorems about their behaviour.
Notable discoveries include that agent behaviour can change radically under
positive linear transformations of the reward signal (from suicidal to
dogmatically self-preserving), and that the agent's posterior belief that it
will survive increases over time.