pwc-archive/papers-with-abstracts huggingface dataset:
Fall of Empires: Breaking Byzantine-tolerant SGD by Inner Product Manipulation
Authors: Cong Xie, Sanmi Koyejo, Indranil Gupta
Date: 2019-03-10 00:00:00
Tasks: BIG-bench Machine Learning

Recently, new defense techniques have been developed to tolerate Byzantine
failures for distributed machine learning. The Byzantine model captures workers
that behave arbitrarily, including malicious and compromised workers. In this
paper, we break two prevailing Byzantine-tolerant techniques. Specifically we
show robust aggregation methods for synchronous SGD -- coordinate-wise median
and Krum -- can be broken using new attack strategies based on inner product
manipulation. We prove our results theoretically, as well as show empirical
validation.
Asynchronous Federated Optimization
Authors: Cong Xie, Sanmi Koyejo, Indranil Gupta
Date: 2019-03-10 00:00:00
Tasks: Federated Learning

Federated learning enables training on a massive number of edge devices. To improve flexibility and scalability, we propose a new asynchronous federated optimization algorithm. We prove that the proposed approach has near-linear convergence to a global optimum, for both strongly convex and a restricted family of non-convex problems. Empirical results show that the proposed algorithm converges quickly and tolerates staleness in various applications.
Automated Segmentation of Knee MRI Using Hierarchical Classifiers and Just Enough Interaction Based Learning: Data from Osteoarthritis Initiative
Authors: Satyananda Kashyap, Ipek Oguz, Honghai Zhang, Milan Sonka
Date: 2019-03-10 00:00:00
Tasks: Segmentation

We present a fully automated learning-based approach for segmenting knee
cartilage in the presence of osteoarthritis (OA). The algorithm employs a
hierarchical set of two random forest classifiers. The first is a neighborhood
approximation forest, the output probability map of which is utilized as a
feature set for the second random forest (RF) classifier. The output
probabilities of the hierarchical approach are used as cost functions in a
Layered Optimal Graph Segmentation of Multiple Objects and Surfaces (LOGISMOS).
In this work, we highlight a novel post-processing interaction called
just-enough interaction (JEI) which enables quick and accurate generation of a
large set of training examples. Disjoint sets of 15 and 13 subjects were used
for training and tested on another disjoint set of 53 knee datasets. All images
were acquired using a double echo steady state (DESS) MRI sequence and are from
the osteoarthritis initiative (OAI) database. Segmentation performance using
the learning-based cost function showed significant reduction in segmentation
errors ($p< 0.05$) in comparison with conventional gradient-based cost
functions.
Learning-Based Cost Functions for 3D and 4D Multi-Surface Multi-Object Segmentation of Knee MRI: Data from the Osteoarthritis Initiative
Authors: Satyananda Kashyap, Honghai Zhang, Karan Rao, Milan Sonka
Date: 2019-03-10 00:00:00
Tasks: Image Segmentation, MRI segmentation, Segmentation, Semantic Segmentation

A fully automated knee MRI segmentation method to study osteoarthritis (OA)
was developed using a novel hierarchical set of random forests (RF) classifiers
to learn the appearance of cartilage regions and their boundaries. A
neighborhood approximation forest is used first to provide contextual feature
to the second-level RF classifier that also considers local features and
produces location-specific costs for the layered optimal graph image
segmentation of multiple objects and surfaces (LOGISMOS) framework. Double echo
steady state (DESS) MRIs used in this work originated from the Osteoarthritis
Initiative (OAI) study. Trained on 34 MRIs with varying degrees of OA, the
performance of the learning-based method tested on 108 MRIs showed a
significant reduction in segmentation errors (\emph{p}$<$0.05) compared with
the conventional gradient-based and single-stage RF-learned costs. The 3D
LOGISMOS was extended to longitudinal-3D (4D) to simultaneously segment
multiple follow-up visits of the same patient. As such, data from all
time-points of the temporal sequence contribute information to a single optimal
solution that utilizes both spatial 3D and temporal contexts. 4D LOGISMOS
validation on 108 MRIs from baseline and 12 month follow-up scans of 54
patients showed a significant reduction in segmentation errors
(\emph{p}$<$0.01) compared to 3D. Finally, the potential of 4D LOGISMOS was
further explored on the same 54 patients using 5 annual follow-up scans
demonstrating a significant improvement of measuring cartilage thickness
(\emph{p}$<$0.01) compared to the sequential 3D approach.
Machine Learning Meets Quantitative Planning: Enabling Self-Adaptation in Autonomous Robots
Authors: Pooyan Jamshidi, Javier Cámara, Bradley Schmerl, Christian Kästner, David Garlan
Date: 2019-03-10 00:00:00
Tasks: BIG-bench Machine Learning

Modern cyber-physical systems (e.g., robotics systems) are typically composed
of physical and software components, the characteristics of which are likely to
change over time. Assumptions about parts of the system made at design time may
not hold at run time, especially when a system is deployed for long periods
(e.g., over decades). Self-adaptation is designed to find reconfigurations of
systems to handle such run-time inconsistencies. Planners can be used to find
and enact optimal reconfigurations in such an evolving context. However, for
systems that are highly configurable, such planning becomes intractable due to
the size of the adaptation space. To overcome this challenge, in this paper we
explore an approach that (a) uses machine learning to find Pareto-optimal
configurations without needing to explore every configuration and (b) restricts
the search space to such configurations to make planning tractable. We explore
this in the context of robot missions that need to consider task timeliness and
energy consumption. An independent evaluation shows that our approach results
in high-quality adaptation plans in uncertain and adversarial environments.
Fairness for Robust Log Loss Classification
Authors: Ashkan Rezaei, Rizal Fathony, Omid Memarrast, Brian Ziebart
Date: 2019-03-10 00:00:00
Tasks: Classification, Decision Making, Fairness, General Classification, regression

Developing classification methods with high accuracy that also avoid unfair treatment of different groups has become increasingly important for data-driven decision making in social applications. Many existing methods enforce fairness constraints on a selected classifier (e.g., logistic regression) by directly forming constrained optimizations. We instead re-derive a new classifier from the first principles of distributional robustness that incorporates fairness criteria into a worst-case logarithmic loss minimization. This construction takes the form of a minimax game and produces a parametric exponential family conditional distribution that resembles truncated logistic regression. We present the theoretical benefits of our approach in terms of its convexity and asymptotic convergence. We then demonstrate the practical advantages of our approach on three benchmark fairness datasets.
Rectangular Bounding Process
Authors: Xuhui Fan, Bin Li, Scott Anthony Sisson
Date: 2019-03-10 00:00:00
Proceeding: NeurIPS 2018 12

Stochastic partition models divide a multi-dimensional space into a number of
rectangular regions, such that the data within each region exhibit certain
types of homogeneity. Due to the nature of their partition strategy, existing
partition models may create many unnecessary divisions in sparse regions when
trying to describe data in dense regions. To avoid this problem we introduce a
new parsimonious partition model -- the Rectangular Bounding Process (RBP) --
to efficiently partition multi-dimensional spaces, by employing a bounding
strategy to enclose data points within rectangular bounding boxes. Unlike
existing approaches, the RBP possesses several attractive theoretical
properties that make it a powerful nonparametric partition prior on a
hypercube. In particular, the RBP is self-consistent and as such can be
directly extended from a finite hypercube to infinite (unbounded) space. We
apply the RBP to regression trees and relational models as a flexible partition
prior. The experimental results validate the merit of the RBP {in rich yet
parsimonious expressiveness} compared to the state-of-the-art methods.
Semantics Preserving Adversarial Learning
Authors: Ousmane Amadou Dia, Elnaz Barshan, Reza Babanezhad
Date: 2019-03-10 00:00:00
Tasks: Text Classification

While progress has been made in crafting visually imperceptible adversarial examples, constructing semantically meaningful ones remains a challenge. In this paper, we propose a framework to generate semantics preserving adversarial examples. First, we present a manifold learning method to capture the semantics of the inputs. The motivating principle is to learn the low-dimensional geometric summaries of the inputs via statistical inference. Then, we perturb the elements of the learned manifold using the Gram-Schmidt process to induce the perturbed elements to remain in the manifold. To produce adversarial examples, we propose an efficient algorithm whereby we leverage the semantics of the inputs as a source of knowledge upon which we impose adversarial constraints. We apply our approach on toy data, images and text, and show its effectiveness in producing semantics preserving adversarial examples which evade existing defenses against adversarial attacks.
GNNExplainer: Generating Explanations for Graph Neural Networks
Authors: Rex Ying, Dylan Bourgeois, Jiaxuan You, Marinka Zitnik, Jure Leskovec
Date: 2019-03-10 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: BIG-bench Machine Learning, Explainable artificial intelligence, Graph Classification, Graph Neural Network, Link Prediction

Graph Neural Networks (GNNs) are a powerful tool for machine learning on graphs.GNNs combine node feature information with the graph structure by recursively passing neural messages along edges of the input graph. However, incorporating both graph structure and feature information leads to complex models, and explaining predictions made by GNNs remains unsolved. Here we propose GNNExplainer, the first general, model-agnostic approach for providing interpretable explanations for predictions of any GNN-based model on any graph-based machine learning task. Given an instance, GNNExplainer identifies a compact subgraph structure and a small subset of node features that have a crucial role in GNN's prediction. Further, GNNExplainer can generate consistent and concise explanations for an entire class of instances. We formulate GNNExplainer as an optimization task that maximizes the mutual information between a GNN's prediction and distribution of possible subgraph structures. Experiments on synthetic and real-world graphs show that our approach can identify important graph structures as well as node features, and outperforms baselines by 17.1% on average. GNNExplainer provides a variety of benefits, from the ability to visualize semantically relevant structures to interpretability, to giving insights into errors of faulty GNNs.
A Hybrid GA-PSO Method for Evolving Architecture and Short Connections of Deep Convolutional Neural Networks
Authors: Bin Wang, Yanan sun, Bing Xue, Mengjie Zhang
Date: 2019-03-10 00:00:00
Tasks: General Classification, image-classification, Image Classification

Image classification is a difficult machine learning task, where
Convolutional Neural Networks (CNNs) have been applied for over 20 years in
order to solve the problem. In recent years, instead of the traditional way of
only connecting the current layer with its next layer, shortcut connections
have been proposed to connect the current layer with its forward layers apart
from its next layer, which has been proved to be able to facilitate the
training process of deep CNNs. However, there are various ways to build the
shortcut connections, it is hard to manually design the best shortcut
connections when solving a particular problem, especially given the design of
the network architecture is already very challenging.
  In this paper, a hybrid evolutionary computation (EC) method is proposed to
\textit{automatically} evolve both the architecture of deep CNNs and the
shortcut connections. Three major contributions of this work are: Firstly, a
new encoding strategy is proposed to encode a CNN, where the architecture and
the shortcut connections are encoded separately; Secondly, a hybrid two-level
EC method, which combines particle swarm optimisation and genetic algorithms,
is developed to search for the optimal CNNs; Lastly, an adjustable learning
rate is introduced for the fitness evaluations, which provides a better
learning rate for the training process given a fixed number of epochs. The
proposed algorithm is evaluated on three widely used benchmark datasets of
image classification and compared with 12 peer Non-EC based competitors and one
EC based competitor. The experimental results demonstrate that the proposed
method outperforms all of the peer competitors in terms of classification
accuracy.
Non-Negative Kernel Sparse Coding for the Classification of Motion Data
Authors: Babak Hosseini, Felix Hülsmann, Mario Botsch, Barbara Hammer
Date: 2019-03-10 00:00:00
Tasks: Dynamic Time Warping, General Classification

We are interested in the decomposition of motion data into a sparse linear
combination of base functions which enable efficient data processing. We
combine two prominent frameworks: dynamic time warping (DTW), which offers
particularly successful pairwise motion data comparison, and sparse coding
(SC), which enables an automatic decomposition of vectorial data into a sparse
linear combination of base vectors. We enhance SC as follows: an efficient
kernelization which extends its application domain to general similarity data
such as offered by DTW, and its restriction to non-negative linear
representations of signals and base vectors in order to guarantee a meaningful
dictionary. Empirical evaluations on motion capture benchmarks show the
effectiveness of our framework regarding interpretation and discrimination
concerns.
DeepPool: Distributed Model-free Algorithm for Ride-sharing using Deep Reinforcement Learning
Authors: Abubakr Alabbasi, Arnob Ghosh, Vaneet Aggarwal
Date: 2019-03-09 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The success of modern ride-sharing platforms crucially depends on the profit
of the ride-sharing fleet operating companies, and how efficiently the
resources are managed. Further, ride-sharing allows sharing costs and, hence,
reduces the congestion and emission by making better use of vehicle capacities.
In this work, we develop a distributed model-free, DeepPool, that uses deep
Q-network (DQN) techniques to learn optimal dispatch policies by interacting
with the environment. Further, DeepPool efficiently incorporates travel demand
statistics and deep learning models to manage dispatching vehicles for improved
ride sharing services. Using real-world dataset of taxi trip records in New
York City, DeepPool performs better than other strategies, proposed in the
literature, that do not consider ride sharing or do not dispatch the vehicles
to regions where the future demand is anticipated. Finally, DeepPool can adapt
rapidly to dynamic environments since it is implemented in a distributed manner
in which each vehicle solves its own DQN individually without coordination.
Literal or Pedagogic Human? Analyzing Human Model Misspecification in Objective Learning
Authors: Smitha Milli, Anca D. Dragan
Date: 2019-03-09 00:00:00

It is incredibly easy for a system designer to misspecify the objective for an autonomous system ("robot''), thus motivating the desire to have the robot learn the objective from human behavior instead. Recent work has suggested that people have an interest in the robot performing well, and will thus behave pedagogically, choosing actions that are informative to the robot. In turn, robots benefit from interpreting the behavior by accounting for this pedagogy. In this work, we focus on misspecification: we argue that robots might not know whether people are being pedagogic or literal and that it is important to ask which assumption is safer to make. We cast objective learning into the more general form of a common-payoff game between the robot and human, and prove that in any such game literal interpretation is more robust to misspecification. Experiments with human data support our theoretical results and point to the sensitivity of the pedagogic assumption.
Functional Principal Component Analysis for Extrapolating Multi-stream Longitudinal Data
Authors: Seokhyun Chung, Raed Kontar
Date: 2019-03-09 00:00:00

The advance of modern sensor technologies enables collection of multi-stream
longitudinal data where multiple signals from different units are collected in
real-time. In this article, we present a non-parametric approach to predict the
evolution of multi-stream longitudinal data for an in-service unit through
borrowing strength from other historical units. Our approach first decomposes
each stream into a linear combination of eigenfunctions and their corresponding
functional principal component (FPC) scores. A Gaussian process prior for the
FPC scores is then established based on a functional semi-metric that measures
similarities between streams of historical units and the in-service unit.
Finally, an empirical Bayesian updating strategy is derived to update the
established prior using real-time stream data obtained from the in-service
unit. Experiments on synthetic and real world data show that the proposed
framework outperforms state-of-the-art approaches and can effectively account
for heterogeneity as well as achieve high predictive accuracy.
Variational Inference of Joint Models using Multivariate Gaussian Convolution Processes
Authors: Xubo Yue, Raed Kontar
Date: 2019-03-09 00:00:00
Tasks: Variational Inference

We present a non-parametric prognostic framework for individualized event
prediction based on joint modeling of both longitudinal and time-to-event data.
Our approach exploits a multivariate Gaussian convolution process (MGCP) to
model the evolution of longitudinal signals and a Cox model to map
time-to-event data with longitudinal data modeled through the MGCP. Taking
advantage of the unique structure imposed by convolved processes, we provide a
variational inference framework to simultaneously estimate parameters in the
joint MGCP-Cox model. This significantly reduces computational complexity and
safeguards against model overfitting. Experiments on synthetic and real world
data show that the proposed framework outperforms state-of-the art approaches
built on two-stage inference and strong parametric assumptions.
Lipstick on a Pig: Debiasing Methods Cover up Systematic Gender Biases in Word Embeddings But do not Remove Them
Authors: Hila Gonen, Yoav Goldberg
Date: 2019-03-09 00:00:00
Proceeding: NAACL 2019 6
Tasks: Word Embeddings

Word embeddings are widely used in NLP for a vast range of tasks. It was shown that word embeddings derived from text corpora reflect gender biases in society. This phenomenon is pervasive and consistent across different word embedding models, causing serious concern. Several recent works tackle this problem, and propose methods for significantly reducing this gender bias in word embeddings, demonstrating convincing results. However, we argue that this removal is superficial. While the bias is indeed substantially reduced according to the provided bias definition, the actual effect is mostly hiding the bias, not removing it. The gender bias information is still reflected in the distances between "gender-neutralized" words in the debiased embeddings, and can be recovered from them. We present a series of experiments to support this claim, for two debiasing methods. We conclude that existing bias removal techniques are insufficient, and should not be trusted for providing gender-neutral modeling.
A Genetic Programming System with an Epigenetic Mechanism for Traffic Signal Control
Authors: Esteban Ricalde
Date: 2019-03-09 00:00:00
Tasks: Evolutionary Algorithms, Traffic Signal Control

Traffic congestion is an increasing problem in most cities around the world.
It impacts businesses as well as commuters, small cities and large ones in
developing as well as developed economies. One approach to decrease urban
traffic congestion is to optimize the traffic signal behaviour in order to be
adaptive to changes in the traffic conditions. From the perspective of
intelligent transportation systems, this optimization problem is called the
traffic signal control problem and is considered a large combinatorial problem
with high complexity and uncertainty. A novel approach to the traffic signal
control problem is proposed in this thesis. The approach includes a new
mechanism for Genetic Programming inspired by Epigenetics. Epigenetic
mechanisms play an important role in biological processes such as phenotype
differentiation, memory consolidation within generations and environmentally
induced epigenetic modification of behaviour. These properties lead us to
consider the implementation of epigenetic mechanisms as a way to improve the
performance of Evolutionary Algorithms in solution to real-world problems with
dynamic environmental changes, such as the traffic control signal problem. The
epigenetic mechanism proposed was evaluated in four traffic scenarios with
different properties and traffic conditions using two microscopic simulators.
The results of these experiments indicate that Genetic Programming was able to
generate competitive actuated traffic signal controllers for all the scenarios
tested. Furthermore, the use of the epigenetic mechanism improved the
performance of Genetic Programming in all the scenarios. The evolved
controllers adapt to modifications in the traffic density and require less
monitoring and less human interaction than other solutions because they
dynamically adjust the signal behaviour depending on the local traffic
conditions at each intersection.
Recovery Bounds on Class-Based Optimal Transport: A Sum-of-Norms Regularization Framework
Authors: Arman Rahbar, Ashkan Panahi, Morteza Haghir Chehreghani, Devdatt Dubhashi, Hamid Krim
Date: 2019-03-09 00:00:00
Tasks: Domain Adaptation

We develop a novel theoretical framework for understating OT schemes respecting a class structure. For this purpose, we propose a convex OT program with a sum-of-norms regularization term, which provably recovers the underlying class structure under geometric assumptions. Furthermore, we derive an accelerated proximal algorithm with a closed-form projection and proximal operator scheme, thereby affording a more scalable algorithm for computing optimal transport plans. We provide a novel argument for the uniqueness of the optimum even in the absence of strong convexity. Our experiments show that the new regularizer not only results in a better preservation of the class structure in the data but also yields additional robustness to the data geometry, compared to previous regularizers.
The Web is missing an essential part of infrastructure: an Open Web Index
Authors: Dirk Lewandowski
Date: 2019-03-09 00:00:00

A proposal for building an index of the Web that separates the infrastructure
part of the search engine - the index - from the services part that will form
the basis for myriad search engines and other services utilizing Web data on
top of a public infrastructure open to everyone.