pwc-archive/papers-with-abstracts huggingface dataset:
Deep Reinforcement Learning for Tensegrity Robot Locomotion
Authors: Marvin Zhang, Xinyang Geng, Jonathan Bruce, Ken Caluwaerts, Massimo Vespignani, Vytas SunSpiral, Pieter Abbeel, Sergey Levine
Date: 2016-09-28 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Tensegrity robots, composed of rigid rods connected by elastic cables, have a
number of unique properties that make them appealing for use as planetary
exploration rovers. However, control of tensegrity robots remains a difficult
problem due to their unusual structures and complex dynamics. In this work, we
show how locomotion gaits can be learned automatically using a novel extension
of mirror descent guided policy search (MDGPS) applied to periodic locomotion
movements, and we demonstrate the effectiveness of our approach on tensegrity
robot locomotion. We evaluate our method with real-world and simulated
experiments on the SUPERball tensegrity robot, showing that the learned
policies generalize to changes in system parameters, unreliable sensor
measurements, and variation in environmental conditions, including varied
terrains and a range of different gravities. Our experiments demonstrate that
our method not only learns fast, power-efficient feedback policies for rolling
gaits, but that these policies can succeed with only the limited onboard
sensing provided by SUPERball's accelerometers. We compare the learned feedback
policies to learned open-loop policies and hand-engineered controllers, and
demonstrate that the learned policy enables the first continuous, reliable
locomotion gait for the real SUPERball robot. Our code and other supplementary
materials are available from http://rll.berkeley.edu/drl_tensegrity
Structural Data Recognition with Graph Model Boosting
Authors: Tomo Miyazaki, Shinichiro Omachi
Date: 2017-03-08 00:00:00
Tasks: model

This paper presents a novel method for structural data recognition using a
large number of graph models. In general, prevalent methods for structural data
recognition have two shortcomings: 1) Only a single model is used to capture
structural variation. 2) Naive recognition methods are used, such as the
nearest neighbor method. In this paper, we propose strengthening the
recognition performance of these models as well as their ability to capture
structural variation. The proposed method constructs a large number of graph
models and trains decision trees using the models. This paper makes two main
contributions. The first is a novel graph model that can quickly perform
calculations, which allows us to construct several models in a feasible amount
of time. The second contribution is a novel approach to structural data
recognition: graph model boosting. Comprehensive structural variations can be
captured with a large number of graph models constructed in a boosting
framework, and a sophisticated classifier can be formed by aggregating the
decision trees. Consequently, we can carry out structural data recognition with
powerful recognition capability in the face of comprehensive structural
variation. The experiments shows that the proposed method achieves impressive
results and outperforms existing methods on datasets of IAM graph database
repository.
Learning and Policy Search in Stochastic Dynamical Systems with Bayesian Neural Networks
Authors: Stefan Depeweg, José Miguel Hernández-Lobato, Finale Doshi-Velez, Steffen Udluft
Date: 2016-05-23 00:00:00
Tasks: Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Stochastic Optimization

We present an algorithm for model-based reinforcement learning that combines
Bayesian neural networks (BNNs) with random roll-outs and stochastic
optimization for policy learning. The BNNs are trained by minimizing
$\alpha$-divergences, allowing us to capture complicated statistical patterns
in the transition dynamics, e.g. multi-modality and heteroskedasticity, which
are usually missed by other common modeling approaches. We illustrate the
performance of our method by solving a challenging benchmark where model-based
approaches usually fail and by obtaining promising results in a real-world
scenario for controlling a gas turbine.
Gradient Coding
Authors: Rashish Tandon, Qi Lei, Alexandros G. Dimakis, Nikos Karampatziakis
Date: 2016-12-10 00:00:00

We propose a novel coding theoretic framework for mitigating stragglers in
distributed learning. We show how carefully replicating data blocks and coding
across gradients can provide tolerance to failures and stragglers for
Synchronous Gradient Descent. We implement our schemes in python (using MPI) to
run on Amazon EC2, and show how we compare against baseline approaches in
running time and generalization error.
Cost-Optimal Learning of Causal Graphs
Authors: Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath
Date: 2017-03-08 00:00:00
Proceeding: ICML 2017 8
Tasks: Graph Learning

We consider the problem of learning a causal graph over a set of variables
with interventions. We study the cost-optimal causal graph learning problem:
For a given skeleton (undirected version of the causal graph), design the set
of interventions with minimum total cost, that can uniquely identify any causal
graph with the given skeleton. We show that this problem is solvable in
polynomial time. Later, we consider the case when the number of interventions
is limited. For this case, we provide polynomial time algorithms when the
skeleton is a tree or a clique tree. For a general chordal skeleton, we develop
an efficient greedy algorithm, which can be improved when the causal graph
skeleton is an interval graph.
Don't Fear the Bit Flips: Optimized Coding Strategies for Binary Classification
Authors: Frederic Sala, Shahroze Kabir, Guy Van Den Broeck, Lara Dolecek
Date: 2017-03-08 00:00:00
Tasks: Binary Classification, General Classification, Quantization

After being trained, classifiers must often operate on data that has been
corrupted by noise. In this paper, we consider the impact of such noise on the
features of binary classifiers. Inspired by tools for classifier robustness, we
introduce the same classification probability (SCP) to measure the resulting
distortion on the classifier outputs. We introduce a low-complexity estimate of
the SCP based on quantization and polynomial multiplication. We also study
channel coding techniques based on replication error-correcting codes. In
contrast to the traditional channel coding approach, where error-correction is
meant to preserve the data and is agnostic to the application, our schemes
specifically aim to maximize the SCP (equivalently minimizing the distortion of
the classifier output) for the same redundancy overhead.
Generalisation in Named Entity Recognition: A Quantitative Analysis
Authors: Isabelle Augenstein, Leon Derczynski, Kalina Bontcheva
Date: 2017-01-11 00:00:00
Tasks: Diversity, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER

Named Entity Recognition (NER) is a key NLP task, which is all the more
challenging on Web and user-generated content with their diverse and
continuously changing language. This paper aims to quantify how this diversity
impacts state-of-the-art NER methods, by measuring named entity (NE) and
context variability, feature sparsity, and their effects on precision and
recall. In particular, our findings indicate that NER approaches struggle to
generalise in diverse genres with limited training data. Unseen NEs, in
particular, play an important role, which have a higher incidence in diverse
genres such as social media than in more regular genres such as newswire.
Coupled with a higher incidence of unseen features more generally and the lack
of large training corpora, this leads to significantly lower F1 scores for
diverse genres as compared to more regular ones. We also find that leading
systems rely heavily on surface forms found in training data, having problems
generalising beyond these, and offer explanations for this observation.
Deep Voice: Real-time Neural Text-to-Speech
Authors: Sercan O. Arik, Mike Chrzanowski, Adam Coates, Gregory Diamos, Andrew Gibiansky, Yongguo Kang, Xi-An Li, John Miller, Andrew Ng, Jonathan Raiman, Shubho Sengupta, Mohammad Shoeybi
Date: 2017-02-25 00:00:00
Proceeding: ICML 2017 8
Tasks: Audio Synthesis, Boundary Detection, CPU, Feature Engineering, GPU, Grapheme-to-Phoneme Conversion, Speech Synthesis, text-to-speech, Text to Speech

We present Deep Voice, a production-quality text-to-speech system constructed
entirely from deep neural networks. Deep Voice lays the groundwork for truly
end-to-end neural speech synthesis. The system comprises five major building
blocks: a segmentation model for locating phoneme boundaries, a
grapheme-to-phoneme conversion model, a phoneme duration prediction model, a
fundamental frequency prediction model, and an audio synthesis model. For the
segmentation model, we propose a novel way of performing phoneme boundary
detection with deep neural networks using connectionist temporal classification
(CTC) loss. For the audio synthesis model, we implement a variant of WaveNet
that requires fewer parameters and trains faster than the original. By using a
neural network for each component, our system is simpler and more flexible than
traditional text-to-speech systems, where each component requires laborious
feature engineering and extensive domain expertise. Finally, we show that
inference with our system can be performed faster than real time and describe
optimized WaveNet inference kernels on both CPU and GPU that achieve up to 400x
speedups over existing implementations.
Predicting Student Dropout in Higher Education
Authors: Lovenoor Aulck, Nishant Velagapudi, Joshua Blumenstock, Jevin West
Date: 2016-06-20 00:00:00
Tasks: Student dropout

Each year, roughly 30% of first-year students at US baccalaureate
institutions do not return for their second year and over $9 billion is spent
educating these students. Yet, little quantitative research has analyzed the
causes and possible remedies for student attrition. Here, we describe initial
efforts to model student dropout using the largest known dataset on higher
education attrition, which tracks over 32,500 students' demographics and
transcript records at one of the nation's largest public universities. Our
results highlight several early indicators of student attrition and show that
dropout can be accurately predicted even when predictions are based on a single
term of academic transcript data. These results highlight the potential for
machine learning to have an impact on student retention and success while
pointing to several promising directions for future work.
Geometry of 3D Environments and Sum of Squares Polynomials
Authors: Amir Ali Ahmadi, Georgina Hall, Ameesh Makadia, Vikas Sindhwani
Date: 2016-11-22 00:00:00
Tasks: Spatial Reasoning

Motivated by applications in robotics and computer vision, we study problems
related to spatial reasoning of a 3D environment using sublevel sets of
polynomials. These include: tightly containing a cloud of points (e.g.,
representing an obstacle) with convex or nearly-convex basic semialgebraic
sets, computation of Euclidean distances between two such sets, separation of
two convex basic semalgebraic sets that overlap, and tight containment of the
union of several basic semialgebraic sets with a single convex one. We use
algebraic techniques from sum of squares optimization that reduce all these
tasks to semidefinite programs of small size and present numerical experiments
in realistic scenarios.
Horde of Bandits using Gaussian Markov Random Fields
Authors: Sharan Vaswani, Mark Schmidt, Laks. V. S. Lakshmanan
Date: 2017-03-07 00:00:00
Tasks: Clustering, Multi-Armed Bandits, Recommendation Systems, Thompson Sampling

The gang of bandits (GOB) model \cite{cesa2013gang} is a recent contextual
bandits framework that shares information between a set of bandit problems,
related by a known (possibly noisy) graph. This model is useful in problems
like recommender systems where the large number of users makes it vital to
transfer information between users. Despite its effectiveness, the existing GOB
model can only be applied to small problems due to its quadratic
time-dependence on the number of nodes. Existing solutions to combat the
scalability issue require an often-unrealistic clustering assumption. By
exploiting a connection to Gaussian Markov random fields (GMRFs), we show that
the GOB model can be made to scale to much larger graphs without additional
assumptions. In addition, we propose a Thompson sampling algorithm which uses
the recent GMRF sampling-by-perturbation technique, allowing it to scale to
even larger problems (leading to a "horde" of bandits). We give regret bounds
and experimental results for GOB with Thompson sampling and epoch-greedy
algorithms, indicating that these methods are as good as or significantly
better than ignoring the graph or adopting a clustering-based approach.
Finally, when an existing graph is not available, we propose a heuristic for
learning it on the fly and show promising results.
Exploiting Strong Convexity from Data with Primal-Dual First-Order Algorithms
Authors: Jialei Wang, Lin Xiao
Date: 2017-03-07 00:00:00
Proceeding: ICML 2017 8
Tasks: regression

We consider empirical risk minimization of linear predictors with convex loss
functions. Such problems can be reformulated as convex-concave saddle point
problems, and thus are well suitable for primal-dual first-order algorithms.
However, primal-dual algorithms often require explicit strongly convex
regularization in order to obtain fast linear convergence, and the required
dual proximal mapping may not admit closed-form or efficient solution. In this
paper, we develop both batch and randomized primal-dual algorithms that can
exploit strong convexity from data adaptively and are capable of achieving
linear convergence even without regularization. We also present dual-free
variants of the adaptive primal-dual algorithms that do not require computing
the dual proximal mapping, which are especially suitable for logistic
regression.
Online Convex Optimization with Unconstrained Domains and Losses
Authors: Ashok Cutkosky, Kwabena Boahen
Date: 2017-03-07 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Hyperparameter Optimization

We propose an online convex optimization algorithm (RescaledExp) that
achieves optimal regret in the unconstrained setting without prior knowledge of
any bounds on the loss functions. We prove a lower bound showing an exponential
separation between the regret of existing algorithms that require a known bound
on the loss functions and any algorithm that does not require such knowledge.
RescaledExp matches this lower bound asymptotically in the number of
iterations. RescaledExp is naturally hyperparameter-free and we demonstrate
empirically that it matches prior optimization algorithms that require
hyperparameter optimization.
Linguistic Knowledge as Memory for Recurrent Neural Networks
Authors: Bhuwan Dhingra, Zhilin Yang, William W. Cohen, Ruslan Salakhutdinov
Date: 2017-03-07 00:00:00
Tasks: LAMBADA, Reading Comprehension

Training recurrent neural networks to model long term dependencies is
difficult. Hence, we propose to use external linguistic knowledge as an
explicit signal to inform the model which memories it should utilize.
Specifically, external knowledge is used to augment a sequence with typed edges
between arbitrarily distant elements, and the resulting graph is decomposed
into directed acyclic subgraphs. We introduce a model that encodes such graphs
as explicit memory in recurrent neural networks, and use it to model
coreference relations in text. We apply our model to several text comprehension
tasks and achieve new state-of-the-art results on all considered benchmarks,
including CNN, bAbi, and LAMBADA. On the bAbi QA tasks, our model solves 15 out
of the 20 tasks with only 1000 training examples per task. Analysis of the
learned representations further demonstrates the ability of our model to encode
fine-grained entity information across a document.
Multi-Robot Active Information Gathering with Periodic Communication
Authors: Mikko Lauri, Eero Heinänen, Simone Frintrop
Date: 2017-03-07 00:00:00
Tasks: Decision Making

A team of robots sharing a common goal can benefit from coordination of the
activities of team members, helping the team to reach the goal more reliably or
quickly. We address the problem of coordinating the actions of a team of robots
with periodic communication capability executing an information gathering task.
We cast the problem as a multi-agent optimal decision-making problem with an
information theoretic objective function. We show that appropriate techniques
for solving decentralized partially observable Markov decision processes
(Dec-POMDPs) are applicable in such information gathering problems. We quantify
the usefulness of coordinated information gathering through simulation studies,
and demonstrate the feasibility of the method in a real-world target tracking
domain.
The Crossover Process: Learnability and Data Protection from Inference Attacks
Authors: Richard Nock, Giorgio Patrini, Finnian Lattimore, Tiberio Caetano
Date: 2016-06-13 00:00:00
Tasks: Causal Inference, Fairness

It is usual to consider data protection and learnability as conflicting
objectives. This is not always the case: we show how to jointly control
inference --- seen as the attack --- and learnability by a noise-free process
that mixes training examples, the Crossover Process (cp). One key point is that
the cp~is typically able to alter joint distributions without touching on
marginals, nor altering the sufficient statistic for the class. In other words,
it saves (and sometimes improves) generalization for supervised learning, but
can alter the relationship between covariates --- and therefore fool measures
of nonlinear independence and causal inference into misleading ad-hoc
conclusions. For example, a cp~can increase / decrease odds ratios, bring
fairness or break fairness, tamper with disparate impact, strengthen, weaken or
reverse causal directions, change observed statistical measures of dependence.
For each of these, we quantify changes brought by a cp, as well as its
statistical impact on generalization abilities via a new complexity measure
that we call the Rademacher cp~complexity. Experiments on a dozen readily
available domains validate the theory.
Data Noising as Smoothing in Neural Network Language Models
Authors: Ziang Xie, Sida I. Wang, Jiwei Li, Daniel Lévy, Aiming Nie, Dan Jurafsky, Andrew Y. Ng
Date: 2017-03-07 00:00:00
Tasks: Language Modeling, Language Modelling, Machine Translation, Translation

Data noising is an effective technique for regularizing neural network
models. While noising is widely adopted in application domains such as vision
and speech, commonly used noising primitives have not been developed for
discrete sequence-level settings such as language modeling. In this paper, we
derive a connection between input noising in neural network language models and
smoothing in $n$-gram models. Using this connection, we draw upon ideas from
smoothing to develop effective noising schemes. We demonstrate performance
gains when applying the proposed schemes to language modeling and machine
translation. Finally, we provide empirical analysis validating the relationship
between noising and smoothing.
Regularising Non-linear Models Using Feature Side-information
Authors: Amina Mollaysa, Pablo Strasser, Alexandros Kalousis
Date: 2017-03-07 00:00:00
Proceeding: ICML 2017 8
Tasks: feature selection

Very often features come with their own vectorial descriptions which provide
detailed information about their properties. We refer to these vectorial
descriptions as feature side-information. In the standard learning scenario,
input is represented as a vector of features and the feature side-information
is most often ignored or used only for feature selection prior to model
fitting. We believe that feature side-information which carries information
about features intrinsic property will help improve model prediction if used in
a proper way during learning process. In this paper, we propose a framework
that allows for the incorporation of the feature side-information during the
learning of very general model families to improve the prediction performance.
We control the structures of the learned models so that they reflect features
similarities as these are defined on the basis of the side-information. We
perform experiments on a number of benchmark datasets which show significant
predictive performance gains, over a number of baselines, as a result of the
exploitation of the side-information.
Flow Fields: Dense Correspondence Fields for Highly Accurate Large Displacement Optical Flow Estimation
Authors: Christian Bailer, Bertram Taetz, Didier Stricker
Date: 2017-03-07 00:00:00
Proceeding: ICCV 2015
Tasks: Optical Flow Estimation, Patch Matching

Modern large displacement optical flow algorithms usually use an
initialization by either sparse descriptor matching techniques or dense
approximate nearest neighbor fields. While the latter have the advantage of
being dense, they have the major disadvantage of being very outlier-prone as
they are not designed to find the optical flow, but the visually most similar
correspondence. In this article we present a dense correspondence field
approach that is much less outlier-prone and thus much better suited for
optical flow estimation than approximate nearest neighbor fields. Our approach
does not require explicit regularization, smoothing (like median filtering) or
a new data term. Instead we solely rely on patch matching techniques and a
novel multi-scale matching strategy. We also present enhancements for outlier
filtering. We show that our approach is better suited for large displacement
optical flow estimation than modern descriptor matching techniques. We do so by
initializing EpicFlow with our approach instead of their originally used
state-of-the-art descriptor matching technique. We significantly outperform the
original EpicFlow on MPI-Sintel, KITTI 2012, KITTI 2015 and Middlebury. In this
extended article of our former conference publication we further improve our
approach in matching accuracy as well as runtime and present more experiments
and insights.
Stopping GAN Violence: Generative Unadversarial Networks
Authors: Samuel Albanie, Sébastien Ehrhardt, João F. Henriques
Date: 2017-03-07 00:00:00

While the costs of human violence have attracted a great deal of attention
from the research community, the effects of the network-on-network (NoN)
violence popularised by Generative Adversarial Networks have yet to be
addressed. In this work, we quantify the financial, social, spiritual,
cultural, grammatical and dermatological impact of this aggression and address
the issue by proposing a more peaceful approach which we term Generative
Unadversarial Networks (GUNs). Under this framework, we simultaneously train
two models: a generator G that does its best to capture whichever data
distribution it feels it can manage, and a motivator M that helps G to achieve
its dream. Fighting is strictly verboten and both models evolve by learning to
respect their differences. The framework is both theoretically and electrically
grounded in game theory, and can be viewed as a winner-shares-all two-player
game in which both players work as a team to achieve the best score.
Experiments show that by working in harmony, the proposed model is able to
claim both the moral and log-likelihood high ground. Our work builds on a rich
history of carefully argued position-papers, published as anonymous YouTube
comments, which prove that the optimal solution to NoN violence is more GUNs.