pwc-archive/papers-with-abstracts huggingface dataset:
Exponential Weights on the Hypercube in Polynomial Time
Authors: Sudeep Raja Putta, Abhishek Shetty
Date: 2018-06-12 00:00:00

We study a general online linear optimization problem(OLO). At each round, a subset of objects from a fixed universe of $n$ objects is chosen, and a linear cost associated with the chosen subset is incurred. To measure the performance of our algorithms, we use the notion of regret which is the difference between the total cost incurred over all iterations and the cost of the best fixed subset in hindsight. We consider Full Information and Bandit feedback for this problem. This problem is equivalent to OLO on the $\{0,1\}^n$ hypercube. The Exp2 algorithm and its bandit variant are commonly used strategies for this problem. It was previously unknown if it is possible to run Exp2 on the hypercube in polynomial time. In this paper, we present a polynomial time algorithm called PolyExp for OLO on the hypercube. We show that our algorithm is equivalent Exp2 on $\{0,1\}^n$, Online Mirror Descent(OMD), Follow The Regularized Leader(FTRL) and Follow The Perturbed Leader(FTPL) algorithms. We show PolyExp achieves expected regret bound that is a factor of $\sqrt{n}$ better than Exp2 in the full information setting under $L_\infty$ adversarial losses. Because of the equivalence of these algorithms, this implies an improvement on Exp2's regret bound in full information. We also show matching regret lower bounds. Finally, we show how to use PolyExp on the $\{-1,+1\}^n$ hypercube, solving an open problem in Bubeck et al (COLT 2012).
Using Inherent Structures to design Lean 2-layer RBMs
Authors: Abhishek Bansal, Abhinav Anand, Chiranjib Bhattacharyya
Date: 2018-06-12 00:00:00
Proceeding: ICML 2018 7

Understanding the representational power of Restricted Boltzmann Machines
(RBMs) with multiple layers is an ill-understood problem and is an area of
active research. Motivated from the approach of \emph{Inherent Structure
formalism} (Stillinger & Weber, 1982), extensively used in analysing Spin
Glasses, we propose a novel measure called \emph{Inherent Structure Capacity}
(ISC), which characterizes the representation capacity of a fixed architecture
RBM by the expected number of modes of distributions emanating from the RBM
with parameters drawn from a prior distribution. Though ISC is intractable, we
show that for a single layer RBM architecture ISC approaches a finite constant
as number of hidden units are increased and to further improve the ISC, one
needs to add a second layer. Furthermore, we introduce \emph{Lean} RBMs, which
are multi-layer RBMs where each layer can have at-most $O(n)$ units with the
number of visible units being n. We show that for every single layer RBM with
$\Omega(n^{2+r}), r \ge 0$, hidden units there exists a two-layered \emph{lean}
RBM with $\Theta(n^2)$ parameters with the same ISC, establishing that 2 layer
RBMs can achieve the same representational power as single-layer RBMs but using
far fewer number of parameters. To the best of our knowledge, this is the first
result which quantitatively establishes the need for layering.
Acting Thoughts: Towards a Mobile Robotic Service Assistant for Users with Limited Communication Skills
Authors: Felix Burget, Lukas Dominique Josef Fiederer, Daniel Kuhner, Martin Völker, Johannes Aldinger, Robin Tibor Schirrmeister, Chau Do, Joschka Boedecker, Bernhard Nebel, Tonio Ball, Wolfram Burgard
Date: 2017-07-20 00:00:00
Tasks: Brain Computer Interface, Task Planning

As autonomous service robots become more affordable and thus available also
for the general public, there is a growing need for user friendly interfaces to
control the robotic system. Currently available control modalities typically
expect users to be able to express their desire through either touch, speech or
gesture commands. While this requirement is fulfilled for the majority of
users, paralyzed users may not be able to use such systems. In this paper, we
present a novel framework, that allows these users to interact with a robotic
service assistant in a closed-loop fashion, using only thoughts. The
brain-computer interface (BCI) system is composed of several interacting
components, i.e., non-invasive neuronal signal recording and decoding,
high-level task planning, motion and manipulation planning as well as
environment perception. In various experiments, we demonstrate its
applicability and robustness in real world scenarios, considering
fetch-and-carry tasks and tasks involving human-robot interaction. As our
results demonstrate, our system is capable of adapting to frequent changes in
the environment and reliably completing given tasks within a reasonable amount
of time. Combined with high-level planning and autonomous robotic systems,
interesting new perspectives open up for non-invasive BCI-based human-robot
interactions.
Structured Evolution with Compact Architectures for Scalable Policy Optimization
Authors: Krzysztof Choromanski, Mark Rowland, Vikas Sindhwani, Richard E. Turner, Adrian Weller
Date: 2018-04-06 00:00:00
Proceeding: ICML 2018 7
Tasks: OpenAI Gym, Text-to-Image Generation

We present a new method of blackbox optimization via gradient approximation
with the use of structured random orthogonal matrices, providing more accurate
estimators than baselines and with provable theoretical guarantees. We show
that this algorithm can be successfully applied to learn better quality compact
policies than those using standard gradient estimation techniques. The compact
policies we learn have several advantages over unstructured ones, including
faster training algorithms and faster inference. These benefits are important
when the policy is deployed on real hardware with limited resources. Further,
compact policies provide more scalable architectures for derivative-free
optimization (DFO) in high-dimensional spaces. We show that most robotics tasks
from the OpenAI Gym can be solved using neural networks with less than 300
parameters, with almost linear time complexity of the inference phase, with up
to 13x fewer parameters relative to the Evolution Strategies (ES) algorithm
introduced by Salimans et al. (2017). We do not need heuristics such as fitness
shaping to learn good quality policies, resulting in a simple and theoretically
motivated training mechanism.
Benchmarking Evolutionary Algorithms For Single Objective Real-valued Constrained Optimization - A Critical Review
Authors: Michael Hellwig, Hans-Georg Beyer
Date: 2018-06-12 00:00:00
Tasks: Benchmarking, Evolutionary Algorithms

Benchmarking plays an important role in the development of novel search
algorithms as well as for the assessment and comparison of contemporary
algorithmic ideas. This paper presents common principles that need to be taken
into account when considering benchmarking problems for constrained
optimization. Current benchmark environments for testing Evolutionary
Algorithms are reviewed in the light of these principles. Along with this line,
the reader is provided with an overview of the available problem domains in the
field of constrained benchmarking. Hence, the review supports algorithms
developers with information about the merits and demerits of the available
frameworks.
Multi-Agent Deep Reinforcement Learning with Human Strategies
Authors: Thanh Nguyen, Ngoc Duy Nguyen, Saeid Nahavandi
Date: 2018-06-12 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep learning has enabled traditional reinforcement learning methods to deal with high-dimensional problems. However, one of the disadvantages of deep reinforcement learning methods is the limited exploration capacity of learning agents. In this paper, we introduce an approach that integrates human strategies to increase the exploration capacity of multiple deep reinforcement learning agents. We also report the development of our own multi-agent environment called Multiple Tank Defence to simulate the proposed approach. The results show the significant performance improvement of multiple agents that have learned cooperatively with human strategies. This implies that there is a critical need for human intellect teamed with machines to solve complex problems. In addition, the success of this simulation indicates that our multi-agent environment can be used as a testbed platform to develop and validate other multi-agent control algorithms.
An Extension of Averaged-Operator-Based Algorithms
Authors: Miguel Simões, José Bioucas-Dias, Luis B. Almeida
Date: 2018-06-12 00:00:00

Many of the algorithms used to solve minimization problems with
sparsity-inducing regularizers are generic in the sense that they do not take
into account the sparsity of the solution in any particular way. However,
algorithms known as semismooth Newton are able to take advantage of this
sparsity to accelerate their convergence. We show how to extend these
algorithms in different directions, and study the convergence of the resulting
algorithms by showing that they are a particular case of an extension of the
well-known Krasnosel'ski\u{\i}--Mann scheme.
Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis
Authors: Ye Jia, Yu Zhang, Ron J. Weiss, Quan Wang, Jonathan Shen, Fei Ren, Zhifeng Chen, Patrick Nguyen, Ruoming Pang, Ignacio Lopez Moreno, Yonghui Wu
Date: 2018-06-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Speaker Verification, Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis, Transfer Learning, Voice Cloning

Clone a voice in 5 seconds to generate arbitrary speech in real-time
Logistic Ensemble Models
Authors: Bob Vanderheyden, Jennifer Priestley
Date: 2018-06-12 00:00:00
Tasks: BIG-bench Machine Learning, Binary Classification

Predictive models that are developed in a regulated industry or a regulated
application, like determination of credit worthiness, must be interpretable and
rational (e.g., meaningful improvements in basic credit behavior must result in
improved credit worthiness scores). Machine Learning technologies provide very
good performance with minimal analyst intervention, making them well suited to
a high volume analytic environment, but the majority are black box tools that
provide very limited insight or interpretability into key drivers of model
performance or predicted model output values. This paper presents a methodology
that blends one of the most popular predictive statistical modeling methods for
binary classification with a core model enhancement strategy found in machine
learning. The resulting prediction methodology provides solid performance, from
minimal analyst effort, while providing the interpretability and rationality
required in regulated industries, as well as in other environments where
interpretation of model parameters is required (e.g. businesses that require
interpretation of models, to take action on them).
Bitcoin Volatility Forecasting with a Glimpse into Buy and Sell Orders
Authors: Tian Guo, Albert Bifet, Nino Antulov-Fantulin
Date: 2018-02-12 00:00:00
Tasks: BIG-bench Machine Learning

In this paper, we study the ability to make the short-term prediction of the
exchange price fluctuations towards the United States dollar for the Bitcoin
market. We use the data of realized volatility collected from one of the
largest Bitcoin digital trading offices in 2016 and 2017 as well as order
information. Experiments are performed to evaluate a variety of statistical and
machine learning approaches.
Combining Model-Free Q-Ensembles and Model-Based Approaches for Informed Exploration
Authors: Sreecharan Sankaranarayanan, Raghuram Mandyam Annasamy, Katia Sycara, Carolyn Penstein Rosé
Date: 2018-06-12 00:00:00
Tasks: Decoder, model

Q-Ensembles are a model-free approach where input images are fed into
different Q-networks and exploration is driven by the assumption that
uncertainty is proportional to the variance of the output Q-values obtained.
They have been shown to perform relatively well compared to other exploration
strategies. Further, model-based approaches, such as encoder-decoder models
have been used successfully for next frame prediction given previous frames.
This paper proposes to integrate the model-free Q-ensembles and model-based
approaches with the hope of compounding the benefits of both and achieving
superior exploration as a result. Results show that a model-based trajectory
memory approach when combined with Q-ensembles produces superior performance
when compared to only using Q-ensembles.
Adversarial Risk and the Dangers of Evaluating Against Weak Attacks
Authors: Jonathan Uesato, Brendan O'Donoghue, Aaron van den Oord, Pushmeet Kohli
Date: 2018-02-15 00:00:00
Proceeding: ICML 2018 7
Tasks: Adversarial Robustness

This paper investigates recently proposed approaches for defending against
adversarial examples and evaluating adversarial robustness. We motivate
'adversarial risk' as an objective for achieving models robust to worst-case
inputs. We then frame commonly used attacks and evaluation metrics as defining
a tractable surrogate objective to the true adversarial risk. This suggests
that models may optimize this surrogate rather than the true adversarial risk.
We formalize this notion as 'obscurity to an adversary,' and develop tools and
heuristics for identifying obscured models and designing transparent models. We
demonstrate that this is a significant problem in practice by repurposing
gradient-free optimization techniques into adversarial attacks, which we use to
decrease the accuracy of several recently proposed defenses to near zero. Our
hope is that our formulations and results will help researchers to develop more
powerful defenses.
Deep State Space Models for Unconditional Word Generation
Authors: Florian Schmidt, Thomas Hofmann
Date: 2018-06-12 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: State Space Models, Text Generation, Variational Inference

Autoregressive feedback is considered a necessity for successful
unconditional text generation using stochastic sequence models. However, such
feedback is known to introduce systematic biases into the training process and
it obscures a principle of generation: committing to global information and
forgetting local nuances. We show that a non-autoregressive deep state space
model with a clear separation of global and local uncertainty can be built from
only two ingredients: An independent noise source and a deterministic
transition function. Recent advances on flow-based variational inference can be
used to train an evidence lower-bound without resorting to annealing, auxiliary
losses or similar measures. The result is a highly interpretable generative
model on par with comparable auto-regressive models on the task of word
generation.
Early Seizure Detection with an Energy-Efficient Convolutional Neural Network on an Implantable Microcontroller
Authors: Maria Hügle, Simon Heller, Manuel Watter, Manuel Blum, Farrokh Manzouri, Matthias Dümpelmann, Andreas Schulze-Bonhage, Peter Woias, Joschka Boedecker
Date: 2018-06-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG), Seizure Detection

Implantable, closed-loop devices for automated early detection and
stimulation of epileptic seizures are promising treatment options for patients
with severe epilepsy that cannot be treated with traditional means. Most
approaches for early seizure detection in the literature are, however, not
optimized for implementation on ultra-low power microcontrollers required for
long-term implantation. In this paper we present a convolutional neural network
for the early detection of seizures from intracranial EEG signals, designed
specifically for this purpose. In addition, we investigate approximations to
comply with hardware limits while preserving accuracy. We compare our approach
to three previously proposed convolutional neural networks and a feature-based
SVM classifier with respect to detection accuracy, latency and computational
needs. Evaluation is based on a comprehensive database with long-term EEG
recordings. The proposed method outperforms the other detectors with a median
sensitivity of 0.96, false detection rate of 10.1 per hour and median detection
delay of 3.7 seconds, while being the only approach suited to be realized on a
low power microcontroller due to its parsimonious use of computational and
memory resources.
Learning Deep Similarity Metric for 3D MR-TRUS Registration
Authors: Grant Haskins, Jochen Kruecker, Uwe Kruger, Sheng Xu, Peter A. Pinto, Brad J. Wood, Pingkun Yan
Date: 2018-06-12 00:00:00
Tasks: Image Registration

Purpose: The fusion of transrectal ultrasound (TRUS) and magnetic resonance
(MR) images for guiding targeted prostate biopsy has significantly improved the
biopsy yield of aggressive cancers. A key component of MR-TRUS fusion is image
registration. However, it is very challenging to obtain a robust automatic
MR-TRUS registration due to the large appearance difference between the two
imaging modalities. The work presented in this paper aims to tackle this
problem by addressing two challenges: (i) the definition of a suitable
similarity metric and (ii) the determination of a suitable optimization
strategy.
  Methods: This work proposes the use of a deep convolutional neural network to
learn a similarity metric for MR-TRUS registration. We also use a composite
optimization strategy that explores the solution space in order to search for a
suitable initialization for the second-order optimization of the learned
metric. Further, a multi-pass approach is used in order to smooth the metric
for optimization.
  Results: The learned similarity metric outperforms the classical mutual
information and also the state-of-the-art MIND feature based methods. The
results indicate that the overall registration framework has a large capture
range. The proposed deep similarity metric based approach obtained a mean TRE
of 3.86mm (with an initial TRE of 16mm) for this challenging problem.
  Conclusion: A similarity metric that is learned using a deep neural network
can be used to assess the quality of any given image registration and can be
used in conjunction with the aforementioned optimization framework to perform
automatic registration that is robust to poor initialization.
Approximate inference with Wasserstein gradient flows
Authors: Charlie Frogner, Tomaso Poggio
Date: 2018-06-12 00:00:00

We present a novel approximate inference method for diffusion processes,
based on the Wasserstein gradient flow formulation of the diffusion. In this
formulation, the time-dependent density of the diffusion is derived as the
limit of implicit Euler steps that follow the gradients of a particular free
energy functional. Existing methods for computing Wasserstein gradient flows
rely on discretization of the domain of the diffusion, prohibiting their
application to domains in more than several dimensions. We propose instead a
discretization-free inference method that computes the Wasserstein gradient
flow directly in a space of continuous functions. We characterize approximation
properties of the proposed method and evaluate it on a nonlinear filtering
task, finding performance comparable to the state-of-the-art for filtering
diffusions.
Adversarial Attacks on Neural Networks for Graph Data
Authors: Daniel Zügner, Amir Akbarnejad, Stephan Günnemann
Date: 2018-05-21 00:00:00
Tasks: General Classification, Node Classification

Deep learning models for graphs have achieved strong performance for the task of node classification. Despite their proliferation, currently there is no study of their robustness to adversarial attacks. Yet, in domains where they are likely to be used, e.g. the web, adversaries are common. Can deep learning models for graphs be easily fooled? In this work, we introduce the first study of adversarial attacks on attributed graphs, specifically focusing on models exploiting ideas of graph convolutions. In addition to attacks at test time, we tackle the more challenging class of poisoning/causative attacks, which focus on the training phase of a machine learning model. We generate adversarial perturbations targeting the node's features and the graph structure, thus, taking the dependencies between instances in account. Moreover, we ensure that the perturbations remain unnoticeable by preserving important data characteristics. To cope with the underlying discrete domain we propose an efficient algorithm Nettack exploiting incremental computations. Our experimental study shows that accuracy of node classification significantly drops even when performing only few perturbations. Even more, our attacks are transferable: the learned attacks generalize to other state-of-the-art node classification models and unsupervised approaches, and likewise are successful even when only limited knowledge about the graph is given.
Term Definitions Help Hypernymy Detection
Authors: Wenpeng Yin, Dan Roth
Date: 2018-06-12 00:00:00
Proceeding: SEMEVAL 2018 6

Existing methods of hypernymy detection mainly rely on statistics over a big
corpus, either mining some co-occurring patterns like "animals such as cats" or
embedding words of interest into context-aware vectors. These approaches are
therefore limited by the availability of a large enough corpus that can cover
all terms of interest and provide sufficient contextual information to
represent their meaning. In this work, we propose a new paradigm, HyperDef, for
hypernymy detection -- expressing word meaning by encoding word definitions,
along with context driven representation. This has two main benefits: (i)
Definitional sentences express (sense-specific) corpus-independent meanings of
words, hence definition-driven approaches enable strong generalization -- once
trained, the model is expected to work well in open-domain testbeds; (ii)
Global context from a large corpus and definitions provide complementary
information for words. Consequently, our model, HyperDef, once trained on
task-agnostic data, gets state-of-the-art results in multiple benchmarks
Online Parallel Portfolio Selection with Heterogeneous Island Model
Authors: Štěpán Balcar, Martin Pilát
Date: 2018-06-12 00:00:00
Tasks: Evolutionary Algorithms, model

We present an online parallel portfolio selection algorithm based on the
island model commonly used for parallelization of evolutionary algorithms. In
our case each of the islands runs a different optimization algorithm. The
distributed computation is managed by a central planner which periodically
changes the running methods during the execution of the algorithm -- less
successful methods are removed while new instances of more successful methods
are added.
  We compare different types of planners in the heterogeneous island model
among themselves and also to the traditional homogeneous model on a wide set of
problems. The tests include experiments with different representations of the
individuals and different duration of fitness function evaluations. The results
show that heterogeneous models are a more general and universal computational
tool compared to homogeneous models.
Learning to Automatically Generate Fill-In-The-Blank Quizzes
Authors: Edison Marrese-Taylor, Ai Nakajima, Yutaka Matsuo, Ono Yuichi
Date: 2018-06-12 00:00:00
Proceeding: WS 2018 7
Tasks: BIG-bench Machine Learning, Deep Learning, Question Generation, Question-Generation

In this paper we formalize the problem automatic fill-in-the-blank question
generation using two standard NLP machine learning schemes, proposing concrete
deep learning models for each. We present an empirical study based on data
obtained from a language learning platform showing that both of our proposed
settings offer promising results.