pwc-archive/papers-with-abstracts huggingface dataset:
Deep Multi-View Spatial-Temporal Network for Taxi Demand Prediction
Authors: Huaxiu Yao, Fei Wu, Jintao Ke, Xianfeng Tang, Yitian Jia, Siyu Lu, Pinghua Gong, Jieping Ye, Zhenhui Li
Date: 2018-02-23 00:00:00
Tasks: image-classification, Image Classification, Prediction, Time Series Analysis, Time Series Forecasting, Traffic Prediction

Taxi demand prediction is an important building block to enabling intelligent
transportation systems in a smart city. An accurate prediction model can help
the city pre-allocate resources to meet travel demand and to reduce empty taxis
on streets which waste energy and worsen the traffic congestion. With the
increasing popularity of taxi requesting services such as Uber and Didi Chuxing
(in China), we are able to collect large-scale taxi demand data continuously.
How to utilize such big data to improve the demand prediction is an interesting
and critical real-world problem. Traditional demand prediction methods mostly
rely on time series forecasting techniques, which fail to model the complex
non-linear spatial and temporal relations. Recent advances in deep learning
have shown superior performance on traditionally challenging tasks such as
image classification by learning the complex features and correlations from
large-scale data. This breakthrough has inspired researchers to explore deep
learning techniques on traffic prediction problems. However, existing methods
on traffic prediction have only considered spatial relation (e.g., using CNN)
or temporal relation (e.g., using LSTM) independently. We propose a Deep
Multi-View Spatial-Temporal Network (DMVST-Net) framework to model both spatial
and temporal relations. Specifically, our proposed model consists of three
views: temporal view (modeling correlations between future demand values with
near time points via LSTM), spatial view (modeling local spatial correlation
via local CNN), and semantic view (modeling correlations among regions sharing
similar temporal patterns). Experiments on large-scale real taxi demand data
demonstrate effectiveness of our approach over state-of-the-art methods.
Inference in Sparse Graphs with Pairwise Measurements and Side Information
Authors: Dylan J. Foster, Daniel Reichman, Karthik Sridharan
Date: 2017-03-08 00:00:00
Tasks: Learning Theory, Tree Decomposition

We consider the statistical problem of recovering a hidden "ground truth"
binary labeling for the vertices of a graph up to low Hamming error from noisy
edge and vertex measurements. We present new algorithms and a sharp
finite-sample analysis for this problem on trees and sparse graphs with poor
expansion properties such as hypergrids and ring lattices. Our method
generalizes and improves over that of Globerson et al. (2015), who introduced
the problem for two-dimensional grid lattices.
  For trees we provide a simple, efficient, algorithm that infers the ground
truth with optimal Hamming error has optimal sample complexity and implies
recovery results for all connected graphs. Here, the presence of side
information is critical to obtain a non-trivial recovery rate. We then show how
to adapt this algorithm to tree decompositions of edge-subgraphs of certain
graph families such as lattices, resulting in optimal recovery error rates that
can be obtained efficiently
  The thrust of our analysis is to 1) use the tree decomposition along with
edge measurements to produce a small class of viable vertex labelings and 2)
apply an analysis influenced by statistical learning theory to show that we can
infer the ground truth from this class using vertex measurements. We show the
power of our method in several examples including hypergrids, ring lattices,
and the Newman-Watts model for small world graphs. For two-dimensional grids,
our results improve over Globerson et al. (2015) by obtaining optimal recovery
in the constant-height regime.
A Multi-Disciplinary Review of Knowledge Acquisition Methods: From Human to Autonomous Eliciting Agents
Authors: George Leu, Hussein Abbass
Date: 2018-02-27 00:00:00
Tasks: General Classification, Red Teaming

This paper offers a multi-disciplinary review of knowledge acquisition
methods in human activity systems. The review captures the degree of
involvement of various types of agencies in the knowledge acquisition process,
and proposes a classification with three categories of methods: the human
agent, the human-inspired agent, and the autonomous machine agent methods. In
the first two categories, the acquisition of knowledge is seen as a cognitive
task analysis exercise, while in the third category knowledge acquisition is
treated as an autonomous knowledge-discovery endeavour. The motivation for this
classification stems from the continuous change over time of the structure,
meaning and purpose of human activity systems, which are seen as the factor
that fuelled researchers' and practitioners' efforts in knowledge acquisition
for more than a century.
  We show through this review that the KA field is increasingly active due to
the higher and higher pace of change in human activity, and conclude by
discussing the emergence of a fourth category of knowledge acquisition methods,
which are based on red-teaming and co-evolution.
Annotating High-Level Structures of Short Stories and Personal Anecdotes
Authors: Boyang Li, Beth Cardier, Tong Wang, Florian Metze
Date: 2017-10-08 00:00:00
Proceeding: LREC 2018 5
Tasks: Cultural Vocal Bursts Intensity Prediction, Vocal Bursts Intensity Prediction

Stories are a vital form of communication in human culture; they are employed
daily to persuade, to elicit sympathy, or to convey a message. Computational
understanding of human narratives, especially high-level narrative structures,
remain limited to date. Multiple literary theories for narrative structures
exist, but operationalization of the theories has remained a challenge. We
developed an annotation scheme by consolidating and extending existing
narratological theories, including Labov and Waletsky's (1967) functional
categorization scheme and Freytag's (1863) pyramid of dramatic tension, and
present 360 annotated short stories collected from online sources. In the
future, this research will support an approach that enables systems to
intelligently sustain complex communications with humans.
Not-So-Random Features
Authors: Brian Bullins, Cyril Zhang, Yi Zhang
Date: 2017-10-27 00:00:00
Proceeding: ICLR 2018 1
Tasks: Translation

We propose a principled method for kernel learning, which relies on a
Fourier-analytic characterization of translation-invariant or
rotation-invariant kernels. Our method produces a sequence of feature maps,
iteratively refining the SVM margin. We provide rigorous guarantees for
optimality and generalization, interpreting our algorithm as online
equilibrium-finding dynamics in a certain two-player min-max game. Evaluations
on synthetic and real-world datasets demonstrate scalability and consistent
improvements over related random features-based methods.
Computational Red Teaming in a Sudoku Solving Context: Neural Network Based Skill Representation and Acquisition
Authors: George Leu, Hussein Abbass
Date: 2018-02-27 00:00:00
Tasks: Red Teaming

In this paper we provide an insight into the skill representation, where
skill representation is seen as an essential part of the skill assessment stage
in the Computational Red Teaming process. Skill representation is demonstrated
in the context of Sudoku puzzle, for which the real human skills used in Sudoku
solving, along with their acquisition, are represented computationally in a
cognitively plausible manner, by using feed-forward neural networks with
back-propagation, and supervised learning. The neural network based skills are
then coupled with a hard-coded constraint propagation computational Sudoku
solver, in which the solving sequence is kept hard-coded, and the skills are
represented through neural networks. The paper demonstrates that the modified
solver can achieve different levels of proficiency, depending on the amount of
skills acquired through the neural networks. Results are encouraging for
developing more complex skill and skill acquisition models usable in general
frameworks related to the skill assessment aspect of Computational Red Teaming.
Semantic segmentation of trajectories with agent models
Authors: Daisuke Ogawa, Toru Tamaki, Bisser Raytchev, Kazufumi Kaneda
Date: 2018-02-27 00:00:00
Tasks: Clustering, General Classification, Semantic Segmentation

In many cases, such as trajectories clustering and classification, we often
divide a trajectory into segments as preprocessing. In this paper, we propose a
trajectory semantic segmentation method based on learned behavior models. In
the proposed method, we learn some behavior models from video sequences. Next,
using learned behavior models and a hidden Markov model, we segment a
trajectory into semantic segments. Comparing with the Ramer-Douglas-Peucker
algorithm, we show the effectiveness of the proposed method.
Learning Binary Latent Variable Models: A Tensor Eigenpair Approach
Authors: Ariel Jaffe, Roi Weiss, Shai Carmi, Yuval Kluger, Boaz Nadler
Date: 2018-02-27 00:00:00
Proceeding: ICML 2018 7
Tasks: Tensor Decomposition

Latent variable models with hidden binary units appear in various
applications. Learning such models, in particular in the presence of noise, is
a challenging computational problem. In this paper we propose a novel spectral
approach to this problem, based on the eigenvectors of both the second order
moment matrix and third order moment tensor of the observed data. We prove that
under mild non-degeneracy conditions, our method consistently estimates the
model parameters at the optimal parametric rate. Our tensor-based method
generalizes previous orthogonal tensor decomposition approaches, where the
hidden units were assumed to be either statistically independent or mutually
exclusive. We illustrate the consistency of our method on simulated data and
demonstrate its usefulness in learning a common model for population mixtures
in genetics.
Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network
Authors: Zizhao Zhang, Lin Yang, Yefeng Zheng
Date: 2018-02-27 00:00:00
Proceeding: CVPR 2018 6
Tasks: Computed Tomography (CT), Generative Adversarial Network, Image Generation, Image Registration, Segmentation

Synthesized medical images have several important applications, e.g., as an
intermedium in cross-modality image registration and as supplementary training
samples to boost the generalization capability of a classifier. Especially,
synthesized computed tomography (CT) data can provide X-ray attenuation map for
radiation therapy planning. In this work, we propose a generic cross-modality
synthesis approach with the following targets: 1) synthesizing realistic
looking 3D images using unpaired training data, 2) ensuring consistent
anatomical structures, which could be changed by geometric distortion in
cross-modality synthesis and 3) improving volume segmentation by using
synthetic data for modalities with limited training samples. We show that these
goals can be achieved with an end-to-end 3D convolutional neural network (CNN)
composed of mutually-beneficial generators and segmentors for image synthesis
and segmentation tasks. The generators are trained with an adversarial loss, a
cycle-consistency loss, and also a shape-consistency loss, which is supervised
by segmentors, to reduce the geometric distortion. From the segmentation view,
the segmentors are boosted by synthetic data from generators in an online
manner. Generators and segmentors prompt each other alternatively in an
end-to-end training fashion. With extensive experiments on a dataset including
a total of 4,496 CT and magnetic resonance imaging (MRI) cardiovascular
volumes, we show both tasks are beneficial to each other and coupling these two
tasks results in better performance than solving them exclusively.
On the Suitability of $L_p$-norms for Creating and Preventing Adversarial Examples
Authors: Mahmood Sharif, Lujo Bauer, Michael K. Reiter
Date: 2018-02-27 00:00:00
Tasks: Perceptual Distance

Much research effort has been devoted to better understanding adversarial
examples, which are specially crafted inputs to machine-learning models that
are perceptually similar to benign inputs, but are classified differently
(i.e., misclassified). Both algorithms that create adversarial examples and
strategies for defending against them typically use $L_p$-norms to measure the
perceptual similarity between an adversarial input and its benign original.
Prior work has already shown, however, that two images need not be close to
each other as measured by an $L_p$-norm to be perceptually similar. In this
work, we show that nearness according to an $L_p$-norm is not just unnecessary
for perceptual similarity, but is also insufficient. Specifically, focusing on
datasets (CIFAR10 and MNIST), $L_p$-norms, and thresholds used in prior work,
we show through online user studies that "adversarial examples" that are closer
to their benign counterparts than required by commonly used $L_p$-norm
thresholds can nevertheless be perceptually different to humans from the
corresponding benign examples. Namely, the perceptual distance between two
images that are "near" each other according to an $L_p$-norm can be high enough
that participants frequently classify the two images as representing different
objects or digits. Combined with prior work, we thus demonstrate that nearness
of inputs as measured by $L_p$-norms is neither necessary nor sufficient for
perceptual similarity, which has implications for both creating and defending
against adversarial examples. We propose and discuss alternative similarity
metrics to stimulate future research in the area.
ABC Samplers
Authors: Y. Fan, S. A. Sisson
Date: 2018-02-26 00:00:00

This Chapter, "ABC Samplers", is to appear in the forthcoming Handbook of
Approximate Bayesian Computation (2018). It details the main ideas and
algorithms used to sample from the ABC approximation to the posterior
distribution, including methods based on rejection/importance sampling, MCMC
and sequential Monte Carlo.
Shaping Influence and Influencing Shaping: A Computational Red Teaming Trust-based Swarm Intelligence Model
Authors: Jiangjun Tang, Eleni Petraki, Hussein Abbass
Date: 2018-02-26 00:00:00
Tasks: Red Teaming

Sociotechnical systems are complex systems, where nonlinear interaction among
different players can obscure causal relationships. The absence of mechanisms
to help us understand how to create a change in the system makes it hard to
manage these systems.
  Influencing and shaping are social operators acting on sociotechnical systems
to design a change. However, the two operators are usually discussed in an
ad-hoc manner, without proper guiding models and metrics which assist in
adopting these models successfully. Moreover, both social operators rely on
accurate understanding of the concept of trust. Without such understanding,
neither of these operators can create the required level to create a change in
a desirable direction.
  In this paper, we define these concepts in a concise manner suitable for
modelling the concepts and understanding their dynamics. We then introduce a
model for influencing and shaping and use Computational Red Teaming principles
to design and demonstrate how this model operates. We validate the results
computationally through a simulation environment to show social influencing and
shaping in an artificial society.
Optimizing over a Restricted Policy Class in Markov Decision Processes
Authors: Ershad Banijamali, Yasin Abbasi-Yadkori, Mohammad Ghavamzadeh, Nikos Vlassis
Date: 2018-02-26 00:00:00
Tasks: Policy Gradient Methods

We address the problem of finding an optimal policy in a Markov decision
process under a restricted policy class defined by the convex hull of a set of
base policies. This problem is of great interest in applications in which a
number of reasonably good (or safe) policies are already known and we are only
interested in optimizing in their convex hull. We show that this problem is
NP-hard to solve exactly as well as to approximate to arbitrary accuracy.
However, under a condition that is akin to the occupancy measures of the base
policies having large overlap, we show that there exists an efficient algorithm
that finds a policy that is almost as good as the best convex combination of
the base policies. The running time of the proposed algorithm is linear in the
number of states and polynomial in the number of base policies. In practice, we
demonstrate an efficient implementation for large state problems. Compared to
traditional policy gradient methods, the proposed approach has the advantage
that, apart from the computation of occupancy measures of some base policies,
the iterative method need not interact with the environment during the
optimization process. This is especially important in complex systems where
estimating the value of a policy can be a time consuming process.
Bayesian shape modelling of cross-sectional geological data
Authors: Thomai Tsiftsi, Ian H. Jermyn, Jochen Einbeck
Date: 2018-02-26 00:00:00
Tasks: Sand

Shape information is of great importance in many applications. For example,
the oil-bearing capacity of sand bodies, the subterranean remnants of ancient
rivers, is related to their cross-sectional shapes. The analysis of these
shapes is therefore of some interest, but current classifications are
simplistic and ad hoc. In this paper, we describe the first steps towards a
coherent statistical analysis of these shapes by deriving the integrated
likelihood for data shapes given class parameters. The result is of interest
beyond this particular application.
Convergence Rates for Deterministic and Stochastic Subgradient Methods Without Lipschitz Continuity
Authors: Benjamin Grimmer
Date: 2017-12-12 00:00:00

We extend the classic convergence rate theory for subgradient methods to
apply to non-Lipschitz functions. For the deterministic projected subgradient
method, we present a global $O(1/\sqrt{T})$ convergence rate for any convex
function which is locally Lipschitz around its minimizers. This approach is
based on Shor's classic subgradient analysis and implies generalizations of the
standard convergence rates for gradient descent on functions with Lipschitz or
H\"older continuous gradients. Further, we show a $O(1/\sqrt{T})$ convergence
rate for the stochastic projected subgradient method on convex functions with
at most quadratic growth, which improves to $O(1/T)$ under either strong
convexity or a weaker quadratic lower bound condition.
Learning to select computations
Authors: Frederick Callaway, Sayan Gul, Paul M. Krueger, Thomas L. Griffiths, Falk Lieder
Date: 2017-11-18 00:00:00
Tasks: Management

The efficient use of limited computational resources is an essential
ingredient of intelligence. Selecting computations optimally according to
rational metareasoning would achieve this, but this is computationally
intractable. Inspired by psychology and neuroscience, we propose the first
concrete and domain-general learning algorithm for approximating the optimal
selection of computations: Bayesian metalevel policy search (BMPS). We derive
this general, sample-efficient search algorithm for a computation-selecting
metalevel policy based on the insight that the value of information lies
between the myopic value of information and the value of perfect information.
We evaluate BMPS on three increasingly difficult metareasoning problems: when
to terminate computation, how to allocate computation between competing
options, and planning. Across all three domains, BMPS achieved near-optimal
performance and compared favorably to previously proposed metareasoning
heuristics. Finally, we demonstrate the practical utility of BMPS in an
emergency management scenario, even accounting for the overhead of
metareasoning.
A Fast Deep Learning Model for Textual Relevance in Biomedical Information Retrieval
Authors: Sunil Mohan, Nicolas Fiorini, Sun Kim, Zhiyong Lu
Date: 2018-02-26 00:00:00
Tasks: Biomedical Information Retrieval, Deep Learning, Information Retrieval, Retrieval, Word Embeddings

Publications in the life sciences are characterized by a large technical
vocabulary, with many lexical and semantic variations for expressing the same
concept. Towards addressing the problem of relevance in biomedical literature
search, we introduce a deep learning model for the relevance of a document's
text to a keyword style query. Limited by a relatively small amount of training
data, the model uses pre-trained word embeddings. With these, the model first
computes a variable-length Delta matrix between the query and document,
representing a difference between the two texts, which is then passed through a
deep convolution stage followed by a deep feed-forward network to compute a
relevance score. This results in a fast model suitable for use in an online
search engine. The model is robust and outperforms comparable state-of-the-art
deep learning approaches.
MILE: A Multi-Level Framework for Scalable Graph Embedding
Authors: Jiongqian Liang, Saket Gurukar, Srinivasan Parthasarathy
Date: 2018-02-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Graph Embedding, Node Classification

Recently there has been a surge of interest in designing graph embedding methods. Few, if any, can scale to a large-sized graph with millions of nodes due to both computational complexity and memory requirements. In this paper, we relax this limitation by introducing the MultI-Level Embedding (MILE) framework -- a generic methodology allowing contemporary graph embedding methods to scale to large graphs. MILE repeatedly coarsens the graph into smaller ones using a hybrid matching technique to maintain the backbone structure of the graph. It then applies existing embedding methods on the coarsest graph and refines the embeddings to the original graph through a graph convolution neural network that it learns. The proposed MILE framework is agnostic to the underlying graph embedding techniques and can be applied to many existing graph embedding methods without modifying them. We employ our framework on several popular graph embedding techniques and conduct embedding for real-world graphs. Experimental results on five large-scale datasets demonstrate that MILE significantly boosts the speed (order of magnitude) of graph embedding while generating embeddings of better quality, for the task of node classification. MILE can comfortably scale to a graph with 9 million nodes and 40 million edges, on which existing methods run out of memory or take too long to compute on a modern workstation. Our code and data are publicly available with detailed instructions for adding new base embedding methods: \url{https://github.com/jiongqian/MILE}.
MVG Mechanism: Differential Privacy under Matrix-Valued Query
Authors: Thee Chanyaswad, Alex Dytso, H. Vincent Poor, Prateek Mittal
Date: 2018-01-02 00:00:00

Differential privacy mechanism design has traditionally been tailored for a
scalar-valued query function. Although many mechanisms such as the Laplace and
Gaussian mechanisms can be extended to a matrix-valued query function by adding
i.i.d. noise to each element of the matrix, this method is often suboptimal as
it forfeits an opportunity to exploit the structural characteristics typically
associated with matrix analysis. To address this challenge, we propose a novel
differential privacy mechanism called the Matrix-Variate Gaussian (MVG)
mechanism, which adds a matrix-valued noise drawn from a matrix-variate
Gaussian distribution, and we rigorously prove that the MVG mechanism preserves
$(\epsilon,\delta)$-differential privacy. Furthermore, we introduce the concept
of directional noise made possible by the design of the MVG mechanism.
Directional noise allows the impact of the noise on the utility of the
matrix-valued query function to be moderated. Finally, we experimentally
demonstrate the performance of our mechanism using three matrix-valued queries
on three privacy-sensitive datasets. We find that the MVG mechanism notably
outperforms four previous state-of-the-art approaches, and provides comparable
utility to the non-private baseline.