pwc-archive/papers-with-abstracts huggingface dataset:
HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answering
Authors: Zhilin Yang, Peng Qi, Saizheng Zhang, Yoshua Bengio, William W. Cohen, Ruslan Salakhutdinov, Christopher D. Manning
Date: 2018-09-25 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Multi-hop Question Answering, Question Answering, Sentence

Existing question answering (QA) datasets fail to train QA systems to perform
complex reasoning and provide explanations for answers. We introduce HotpotQA,
a new dataset with 113k Wikipedia-based question-answer pairs with four key
features: (1) the questions require finding and reasoning over multiple
supporting documents to answer; (2) the questions are diverse and not
constrained to any pre-existing knowledge bases or knowledge schemas; (3) we
provide sentence-level supporting facts required for reasoning, allowing QA
systems to reason with strong supervision and explain the predictions; (4) we
offer a new type of factoid comparison questions to test QA systems' ability to
extract relevant facts and perform necessary comparison. We show that HotpotQA
is challenging for the latest QA systems, and the supporting facts enable
models to improve performance and make explainable predictions.
Contextual Bandits with Cross-learning
Authors: Santiago Balseiro, Negin Golrezaei, Mohammad Mahdian, Vahab Mirrokni, Jon Schneider
Date: 2018-09-25 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: Multi-Armed Bandits

In the classical contextual bandits problem, in each round $t$, a learner observes some context $c$, chooses some action $i$ to perform, and receives some reward $r_{i,t}(c)$. We consider the variant of this problem where in addition to receiving the reward $r_{i,t}(c)$, the learner also learns the values of $r_{i,t}(c')$ for some other contexts $c'$ in set $\mathcal{O}_i(c)$; i.e., the rewards that would have been achieved by performing that action under different contexts $c'\in \mathcal{O}_i(c)$. This variant arises in several strategic settings, such as learning how to bid in non-truthful repeated auctions, which has gained a lot of attention lately as many platforms have switched to running first-price auctions. We call this problem the contextual bandits problem with cross-learning. The best algorithms for the classical contextual bandits problem achieve $\tilde{O}(\sqrt{CKT})$ regret against all stationary policies, where $C$ is the number of contexts, $K$ the number of actions, and $T$ the number of rounds. We design and analyze new algorithms for the contextual bandits problem with cross-learning and show that their regret has better dependence on the number of contexts. Under complete cross-learning where the rewards for all contexts are learned when choosing an action, i.e., set $\mathcal{O}_i(c)$ contains all contexts, we show that our algorithms achieve regret $\tilde{O}(\sqrt{KT})$, removing the dependence on $C$. For any other cases, i.e., under partial cross-learning where $|\mathcal{O}_i(c)|< C$ for some context-action pair of $(i,c)$, the regret bounds depend on how the sets $\mathcal O_i(c)$ impact the degree to which cross-learning between contexts is possible. We simulate our algorithms on real auction data from an ad exchange running first-price auctions and show that they outperform traditional contextual bandit algorithms.
Combined convolutional and recurrent neural networks for hierarchical classification of images
Authors: Jaehoon Koo, Diego Klabjan, Jean Utke
Date: 2018-09-25 00:00:00
Tasks: General Classification, image-classification, Image Classification

Deep learning models based on CNNs are predominantly used in image classification tasks. Such approaches, assuming independence of object categories, normally use a CNN as a feature learner and apply a flat classifier on top of it. Object classes in many settings have hierarchical relations, and classifiers exploiting these relations should perform better. We propose hierarchical classification models combining a CNN to extract hierarchical representations of images, and an RNN or sequence-to-sequence model to capture a hierarchical tree of classes. In addition, we apply residual learning to the RNN part in oder to facilitate training our compound model and improve generalization of the model. Experimental results on a real world proprietary dataset of images show that our hierarchical networks perform better than state-of-the-art CNNs.
Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview
Authors: Yuejie Chi, Yue M. Lu, Yuxin Chen
Date: 2018-09-25 00:00:00
Tasks: Matrix Completion, Retrieval

Substantial progress has been made recently on developing provably accurate and efficient algorithms for low-rank matrix factorization via nonconvex optimization. While conventional wisdom often takes a dim view of nonconvex optimization algorithms due to their susceptibility to spurious local minima, simple iterative methods such as gradient descent have been remarkably successful in practice. The theoretical footings, however, had been largely lacking until recently. In this tutorial-style overview, we highlight the important role of statistical models in enabling efficient nonconvex optimization with performance guarantees. We review two contrasting approaches: (1) two-stage algorithms, which consist of a tailored initialization step followed by successive refinement; and (2) global landscape analysis and initialization-free algorithms. Several canonical matrix factorization problems are discussed, including but not limited to matrix sensing, phase retrieval, matrix completion, blind deconvolution, robust principal component analysis, phase synchronization, and joint alignment. Special care is taken to illustrate the key technical insights underlying their analyses. This article serves as a testament that the integrated consideration of optimization and statistics leads to fruitful research findings.
Tangent: Automatic differentiation using source-code transformation for dynamically typed array programming
Authors: Bart van Merriënboer, Dan Moldovan, Alexander B. Wiltschko
Date: 2018-09-25 00:00:00
Proceeding: NeurIPS 2018 12

The need to efficiently calculate first- and higher-order derivatives of
increasingly complex models expressed in Python has stressed or exceeded the
capabilities of available tools. In this work, we explore techniques from the
field of automatic differentiation (AD) that can give researchers expressive
power, performance and strong usability. These include source-code
transformation (SCT), flexible gradient surgery, efficient in-place array
operations, higher-order derivatives as well as mixing of forward and reverse
mode AD. We implement and demonstrate these ideas in the Tangent software
library for Python, the first AD framework for a dynamic language that uses
SCT.
Human-Machine Interface for Remote Training of Robot Tasks
Authors: Jordi Spranger, Roxana Buzatoiu, Athanasios Polydoros, Lazaros Nalpantidis, Evangelos Boukas
Date: 2018-09-25 00:00:00
Tasks: Gesture Recognition, Hand Detection

Regardless of their industrial or research application, the streamlining of
robot operations is limited by the proximity of experienced users to the actual
hardware. Be it massive open online robotics courses, crowd-sourcing of robot
task training, or remote research on massive robot farms for machine learning,
the need to create an apt remote Human-Machine Interface is quite prevalent.
The paper at hand proposes a novel solution to the programming/training of
remote robots employing an intuitive and accurate user-interface which offers
all the benefits of working with real robots without imposing delays and
inefficiency. The system includes: a vision-based 3D hand detection and gesture
recognition subsystem, a simulated digital twin of a robot as visual feedback,
and the "remote" robot learning/executing trajectories using dynamic motion
primitives. Our results indicate that the system is a promising solution to the
problem of remote training of robot tasks.
Coordinating and Integrating Faceted Classification with Rich Semantic Modeling
Authors: Robert B. Allen, Jaihyun Park
Date: 2018-09-25 00:00:00
Tasks: Classification, General Classification

Faceted classifications define dimensions for the types of entities included.
In effect, the facets provide an "ontological commitment". We compare a faceted
thesaurus, the Art and Architecture Thesaurus (AAT), with ontologies derived
from the Basic Formal Ontology (BFO2), which is an upper (or formal) ontology
widely used to describe entities in biomedicine. We consider how the AAT and
BFO2-based ontologies could be coordinated and integrated into a Human Activity
and Infrastructure Foundry (HAIF). To extend the AAT to enable this
coordination and integration, we describe how a wider range of relationships
among its terms could be introduced. Using these extensions, we explore richer
modeling of topics from AAT that deal with Technology. Finally, we consider how
ontology-based frames and semantic role frames can be integrated to make rich
semantic statements about changes in the world.
ComQA: A Community-sourced Dataset for Complex Factoid Question Answering with Paraphrase Clusters
Authors: Abdalghani Abujabal, Rishiraj Saha Roy, Mohamed Yahya, Gerhard Weikum
Date: 2018-09-25 00:00:00
Proceeding: NAACL 2019 6
Tasks: Question Answering

To bridge the gap between the capabilities of the state-of-the-art in factoid
question answering (QA) and what users ask, we need large datasets of real user
questions that capture the various question phenomena users are interested in,
and the diverse ways in which these questions are formulated. We introduce
ComQA, a large dataset of real user questions that exhibit different
challenging aspects such as compositionality, temporal reasoning, and
comparisons. ComQA questions come from the WikiAnswers community QA platform,
which typically contains questions that are not satisfactorily answerable by
existing search engine technology. Through a large crowdsourcing effort, we
clean the question dataset, group questions into paraphrase clusters, and
annotate clusters with their answers. ComQA contains 11,214 questions grouped
into 4,834 paraphrase clusters. We detail the process of constructing ComQA,
including the measures taken to ensure its high quality while making effective
use of crowdsourcing. We also present an extensive analysis of the dataset and
the results achieved by state-of-the-art systems on ComQA, demonstrating that
our dataset can be a driver of future research on QA.
Practical bounds on the error of Bayesian posterior approximations: A nonasymptotic approach
Authors: Jonathan H. Huggins, Trevor Campbell, Mikołaj Kasprzak, Tamara Broderick
Date: 2018-09-25 00:00:00
Tasks: Bayesian Inference, Variational Inference

Bayesian inference typically requires the computation of an approximation to
the posterior distribution. An important requirement for an approximate
Bayesian inference algorithm is to output high-accuracy posterior mean and
uncertainty estimates. Classical Monte Carlo methods, particularly Markov Chain
Monte Carlo, remain the gold standard for approximate Bayesian inference
because they have a robust finite-sample theory and reliable convergence
diagnostics. However, alternative methods, which are more scalable or apply to
problems where Markov Chain Monte Carlo cannot be used, lack the same
finite-data approximation theory and tools for evaluating their accuracy. In
this work, we develop a flexible new approach to bounding the error of mean and
uncertainty estimates of scalable inference algorithms. Our strategy is to
control the estimation errors in terms of Wasserstein distance, then bound the
Wasserstein distance via a generalized notion of Fisher distance. Unlike
computing the Wasserstein distance, which requires access to the normalized
posterior distribution, the Fisher distance is tractable to compute because it
requires access only to the gradient of the log posterior density. We
demonstrate the usefulness of our Fisher distance approach by deriving bounds
on the Wasserstein error of the Laplace approximation and Hilbert coresets. We
anticipate that our approach will be applicable to many other approximate
inference methods such as the integrated Laplace approximation, variational
inference, and approximate Bayesian computation
Anderson Acceleration for Reinforcement Learning
Authors: Matthieu Geist, Bruno Scherrer
Date: 2018-09-25 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Anderson acceleration is an old and simple method for accelerating the
computation of a fixed point. However, as far as we know and quite
surprisingly, it has never been applied to dynamic programming or reinforcement
learning. In this paper, we explain briefly what Anderson acceleration is and
how it can be applied to value iteration, this being supported by preliminary
experiments showing a significant speed up of convergence, that we critically
discuss. We also discuss how this idea could be applied more generally to
(deep) reinforcement learning.
Taking A Closer Look at Domain Shift: Category-level Adversaries for Semantics Consistent Domain Adaptation
Authors: Yawei Luo, Liang Zheng, Tao Guan, Junqing Yu, Yi Yang
Date: 2018-09-25 00:00:00
Proceeding: CVPR 2019 6
Tasks: Domain Adaptation, Semantic Segmentation, Synthetic-to-Real Translation, Unsupervised Domain Adaptation

We consider the problem of unsupervised domain adaptation in semantic
segmentation. The key in this campaign consists in reducing the domain shift,
i.e., enforcing the data distributions of the two domains to be similar. A
popular strategy is to align the marginal distribution in the feature space
through adversarial learning. However, this global alignment strategy does not
consider the local category-level feature distribution. A possible consequence
of the global movement is that some categories which are originally well
aligned between the source and target may be incorrectly mapped. To address
this problem, this paper introduces a category-level adversarial network,
aiming to enforce local semantic consistency during the trend of global
alignment. Our idea is to take a close look at the category-level data
distribution and align each class with an adaptive adversarial loss.
Specifically, we reduce the weight of the adversarial loss for category-level
aligned features while increasing the adversarial force for those poorly
aligned. In this process, we decide how well a feature is category-level
aligned between source and target by a co-training approach. In two domain
adaptation tasks, i.e., GTA5 -> Cityscapes and SYNTHIA -> Cityscapes, we
validate that the proposed method matches the state of the art in segmentation
accuracy.
Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment
Authors: Sergio Pereira, Raphael Meier, Victor Alves, Mauricio Reyes, Carlos A. Silva
Date: 2018-09-25 00:00:00
Tasks: Management, Prognosis

Glioblastoma Multiforme is a high grade, very aggressive, brain tumor, with
patients having a poor prognosis. Lower grade gliomas are less aggressive, but
they can evolve into higher grade tumors over time. Patient management and
treatment can vary considerably with tumor grade, ranging from tumor resection
followed by a combined radio- and chemotherapy to a "wait and see" approach.
Hence, tumor grading is important for adequate treatment planning and
monitoring. The gold standard for tumor grading relies on histopathological
diagnosis of biopsy specimens. However, this procedure is invasive, time
consuming, and prone to sampling error. Given these disadvantages, automatic
tumor grading from widely used MRI protocols would be clinically important, as
a way to expedite treatment planning and assessment of tumor evolution. In this
paper, we propose to use Convolutional Neural Networks for predicting tumor
grade directly from imaging data. In this way, we overcome the need for expert
annotations of regions of interest. We evaluate two prediction approaches: from
the whole brain, and from an automatically defined tumor region. Finally, we
employ interpretability methodologies as a quality assurance stage to check if
the method is using image regions indicative of tumor grade for classification.
Hessian barrier algorithms for linearly constrained optimization problems
Authors: Immanuel M. Bomze, Panayotis Mertikopoulos, Werner Schachinger, Mathias Staudigl
Date: 2018-09-25 00:00:00

In this paper, we propose an interior-point method for linearly constrained optimization problems (possibly nonconvex). The method - which we call the Hessian barrier algorithm (HBA) - combines a forward Euler discretization of Hessian Riemannian gradient flows with an Armijo backtracking step-size policy. In this way, HBA can be seen as an alternative to mirror descent (MD), and contains as special cases the affine scaling algorithm, regularized Newton processes, and several other iterative solution methods. Our main result is that, modulo a non-degeneracy condition, the algorithm converges to the problem's set of critical points; hence, in the convex case, the algorithm converges globally to the problem's minimum set. In the case of linearly constrained quadratic programs (not necessarily convex), we also show that the method's convergence rate is $\mathcal{O}(1/k^\rho)$ for some $\rho\in(0,1]$ that depends only on the choice of kernel function (i.e., not on the problem's primitives). These theoretical results are validated by numerical experiments in standard non-convex test functions and large-scale traffic assignment problems.
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Authors: Jacques Wainer, Gavin Cawley
Date: 2018-09-25 00:00:00

When selecting a classification algorithm to be applied to a particular
problem, one has to simultaneously select the best algorithm for that dataset
\emph{and} the best set of hyperparameters for the chosen model. The usual
approach is to apply a nested cross-validation procedure; hyperparameter
selection is performed in the inner cross-validation, while the outer
cross-validation computes an unbiased estimate of the expected accuracy of the
algorithm \emph{with cross-validation based hyperparameter tuning}. The
alternative approach, which we shall call `flat cross-validation', uses a
single cross-validation step both to select the optimal hyperparameter values
and to provide an estimate of the expected accuracy of the algorithm, that
while biased may nevertheless still be used to select the best learning
algorithm. We tested both procedures using 12 different algorithms on 115 real
life binary datasets and conclude that using the less computationally expensive
flat cross-validation procedure will generally result in the selection of an
algorithm that is, for all practical purposes, of similar quality to that
selected via nested cross-validation, provided the learning algorithms have
relatively few hyperparameters to be optimised.
Fast Automatic Smoothing for Generalized Additive Models
Authors: Yousra El-Bachir, Anthony C. Davison
Date: 2018-09-25 00:00:00
Tasks: Additive models

Multiple generalized additive models (GAMs) are a type of distributional
regression wherein parameters of probability distributions depend on predictors
through smooth functions, with selection of the degree of smoothness via $L_2$
regularization. Multiple GAMs allow finer statistical inference by
incorporating explanatory information in any or all of the parameters of the
distribution. Owing to their nonlinearity, flexibility and interpretability,
GAMs are widely used, but reliable and fast methods for automatic smoothing in
large datasets are still lacking, despite recent advances. We develop a general
methodology for automatically learning the optimal degree of $L_2$
regularization for multiple GAMs using an empirical Bayes approach. The smooth
functions are penalized by different amounts, which are learned simultaneously
by maximization of a marginal likelihood through an approximate
expectation-maximization algorithm that involves a double Laplace approximation
at the E-step, and leads to an efficient M-step. Empirical analysis shows that
the resulting algorithm is numerically stable, faster than all existing methods
and achieves state-of-the-art accuracy. For illustration, we apply it to an
important and challenging problem in the analysis of extremal data.
Defining the Collective Intelligence Supply Chain
Authors: Iain Barclay, Alun Preece, Ian Taylor
Date: 2018-09-25 00:00:00
Tasks: Fairness

Organisations are increasingly open to scrutiny, and need to be able to prove
that they operate in a fair and ethical way. Accountability should extend to
the production and use of the data and knowledge assets used in AI systems, as
it would for any raw material or process used in production of physical goods.
This paper considers collective intelligence, comprising data and knowledge
generated by crowd-sourced workforces, which can be used as core components of
AI systems. A proposal is made for the development of a supply chain model for
tracking the creation and use of crowdsourced collective intelligence assets,
with a blockchain based decentralised architecture identified as an appropriate
means of providing validation, accountability and fairness.
Towards Automated Let's Play Commentary
Authors: Matthew Guzdial, Shukan Shah, Mark Riedl
Date: 2018-09-25 00:00:00
Tasks: BIG-bench Machine Learning

We introduce the problem of generating Let's Play-style commentary of
gameplay video via machine learning. We propose an analysis of Let's Play
commentary and a framework for building such a system. To test this framework
we build an initial, naive implementation, which we use to interrogate the
assumptions of the framework. We demonstrate promising results towards future
Let's Play commentary generation.
Co-Creative Level Design via Machine Learning
Authors: Matthew Guzdial, Nicholas Liao, Mark Riedl
Date: 2018-09-25 00:00:00
Tasks: BIG-bench Machine Learning

Procedural Level Generation via Machine Learning (PLGML), the study of
generating game levels with machine learning, has received a large amount of
recent academic attention. For certain measures these approaches have shown
success at replicating the quality of existing game levels. However, it is
unclear the extent to which they might benefit human designers. In this paper
we present a framework for co-creative level design with a PLGML agent. In
support of this framework we present results from a user study and results from
a comparative study of PLGML approaches.
Explainable PCGML via Game Design Patterns
Authors: Matthew Guzdial, Joshua Reno, Jonathan Chen, Gillian Smith, Mark Riedl
Date: 2018-09-25 00:00:00
Tasks: BIG-bench Machine Learning, Game Design

Procedural content generation via Machine Learning (PCGML) is the umbrella
term for approaches that generate content for games via machine learning. One
of the benefits of PCGML is that, unlike search or grammar-based PCG, it does
not require hand authoring of initial content or rules. Instead, PCGML relies
on existing content and black box models, which can be difficult to tune or
tweak without expert knowledge. This is especially problematic when a human
designer needs to understand how to manipulate their data or models to achieve
desired results. We present an approach to Explainable PCGML via Design
Patterns in which the design patterns act as a vocabulary and mode of
interaction between user and model. We demonstrate that our technique
outperforms non-explainable versions of our system in interactions with five
expert designers, four of whom lack any machine learning expertise.