pwc-archive/papers-with-abstracts huggingface dataset:
Continuous-time Value Function Approximation in Reproducing Kernel Hilbert Spaces
Authors: Motoya Ohnishi, Masahiro Yukawa, Mikael Johansson, Masashi Sugiyama
Date: 2018-06-08 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Atari Games, Gaussian Processes, OpenAI Gym, Reinforcement Learning, Reinforcement Learning (RL)

Motivated by the success of reinforcement learning (RL) for discrete-time
tasks such as AlphaGo and Atari games, there has been a recent surge of
interest in using RL for continuous-time control of physical systems (cf. many
challenging tasks in OpenAI Gym and DeepMind Control Suite). Since
discretization of time is susceptible to error, it is methodologically more
desirable to handle the system dynamics directly in continuous time. However,
very few techniques exist for continuous-time RL and they lack flexibility in
value function approximation. In this paper, we propose a novel framework for
model-based continuous-time value function approximation in reproducing kernel
Hilbert spaces. The resulting framework is so flexible that it can accommodate
any kind of kernel-based approach, such as Gaussian processes and kernel
adaptive filters, and it allows us to handle uncertainties and nonstationarity
without prior knowledge about the environment or what basis functions to
employ. We demonstrate the validity of the presented framework through
experiments.
Classification from Pairwise Similarity and Unlabeled Data
Authors: Han Bao, Gang Niu, Masashi Sugiyama
Date: 2018-02-12 00:00:00
Proceeding: ICML 2018 7
Tasks: Classification, General Classification, Weakly-supervised Learning

Supervised learning needs a huge amount of labeled data, which can be a big
bottleneck under the situation where there is a privacy concern or labeling
cost is high. To overcome this problem, we propose a new weakly-supervised
learning setting where only similar (S) data pairs (two examples belong to the
same class) and unlabeled (U) data points are needed instead of fully labeled
data, which is called SU classification. We show that an unbiased estimator of
the classification risk can be obtained only from SU data, and the estimation
error of its empirical risk minimizer achieves the optimal parametric
convergence rate. Finally, we demonstrate the effectiveness of the proposed
method through experiments.
JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets
Authors: Yunchen Pu, Shuyang Dai, Zhe Gan, Wei-Yao Wang, Guoyin Wang, Yizhe Zhang, Ricardo Henao, Lawrence Carin
Date: 2018-06-08 00:00:00
Proceeding: ICML 2018 7
Tasks: Generative Adversarial Network

A new generative adversarial network is developed for joint distribution
matching. Distinct from most existing approaches, that only learn conditional
distributions, the proposed model aims to learn a joint distribution of
multiple random variables (domains). This is achieved by learning to sample
from conditional distributions between the domains, while simultaneously
learning to sample from the marginals of each individual domain. The proposed
framework consists of multiple generators and a single softmax-based critic,
all jointly trained via adversarial learning. From a simple noise source, the
proposed framework allows synthesis of draws from the marginals, conditional
draws given observations from a subset of random variables, or complete draws
from the full joint distribution. Most examples considered are for joint
analysis of two domains, with examples for three domains also presented.
Monge blunts Bayes: Hardness Results for Adversarial Training
Authors: Zac Cranko, Aditya Krishna Menon, Richard Nock, Cheng Soon Ong, Zhan Shi, Christian Walder
Date: 2018-06-08 00:00:00

The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an adversary which carries out local modifications within prescribed balls. None however has so far questioned the broader picture: how to frame a resource-bounded adversary so that it can be severely detrimental to learning, a non-trivial problem which entails at a minimum the choice of loss and classifiers. We suggest a formal answer for losses that satisfy the minimal statistical requirement of being proper. We pin down a simple sufficient property for any given class of adversaries to be detrimental to learning, involving a central measure of "harmfulness" which generalizes the well-known class of integral probability metrics. A key feature of our result is that it holds for all proper losses, and for a popular subset of these, the optimisation of this central measure appears to be independent of the loss. When classifiers are Lipschitz -- a now popular approach in adversarial training --, this optimisation resorts to optimal transport to make a low-budget compression of class marginals. Toy experiments reveal a finding recently separately observed: training against a sufficiently budgeted adversary of this kind improves generalization.
Fingerprint liveness detection using local quality features
Authors: Ram Prakash Sharma, Somnath Dey
Date: 2018-06-08 00:00:00
Tasks: General Classification

Fingerprint-based recognition has been widely deployed in various
applications. However, current recognition systems are vulnerable to spoofing
attacks which make use of an artificial replica of a fingerprint to deceive the
sensors. In such scenarios, fingerprint liveness detection ensures the actual
presence of a real legitimate fingerprint in contrast to a fake
self-manufactured synthetic sample. In this paper, we propose a static
software-based approach using quality features to detect the liveness in a
fingerprint. We have extracted features from a single fingerprint image to
overcome the issues faced in dynamic software-based approaches which require
longer computational time and user cooperation. The proposed system extracts 8
sensor independent quality features on a local level containing minute details
of the ridge-valley structure of real and fake fingerprints. These local
quality features constitutes a 13-dimensional feature vector. The system is
tested on a publically available dataset of LivDet 2009 competition. The
experimental results exhibit supremacy of the proposed method over current
state-of-the-art approaches providing least average classification error of
5.3% for LivDet 2009. Additionally, effectiveness of the best performing
features over LivDet 2009 is evaluated on the latest LivDet 2015 dataset which
contain fingerprints fabricated using unknown spoof materials. An average
classification error rate of 4.22% is achieved in comparison with 4.49%
obtained by the LivDet 2015 winner. Further, the proposed system utilizes a
single fingerprint image, which results in faster implications and makes it
more user-friendly.
Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Authors: Yi Wu, Siddharth Srivastava, Nicholas Hay, Simon Du, Stuart Russell
Date: 2018-06-06 00:00:00
Proceeding: ICML 2018 7
Tasks: Probabilistic Programming, State Space Models

Despite the recent successes of probabilistic programming languages (PPLs) in
AI applications, PPLs offer only limited support for random variables whose
distributions combine discrete and continuous elements. We develop the notion
of measure-theoretic Bayesian networks (MTBNs) and use it to provide more
general semantics for PPLs with arbitrarily many random variables defined over
arbitrary measure spaces. We develop two new general sampling algorithms that
are provably correct under the MTBN framework: the lexicographic likelihood
weighting (LLW) for general MTBNs and the lexicographic particle filter (LPF),
a specialized algorithm for state-space models. We further integrate MTBNs into
a widely used PPL system, BLOG, and verify the effectiveness of the new
inference algorithms through representative examples.
PAC Ranking from Pairwise and Listwise Queries: Lower Bounds and Upper Bounds
Authors: Wenbo Ren, Jia Liu, Ness B. Shroff
Date: 2018-06-08 00:00:00

This paper explores the adaptive (active) PAC (probably approximately
correct) top-$k$ ranking (i.e., top-$k$ item selection) and total ranking
problems from $l$-wise ($l\geq 2$) comparisons under the multinomial logit
(MNL) model. By adaptively choosing sets to query and observing the noisy
output of the most favored item of each query, we want to design ranking
algorithms that recover the top-$k$ or total ranking using as few queries as
possible. For the PAC top-$k$ ranking problem, we derive a lower bound on the
sample complexity (aka number of queries), and propose an algorithm that is
sample-complexity-optimal up to an $O(\log(k+l)/\log{k})$ factor. When $l=2$
(i.e., pairwise comparisons) or $l=O(poly(k))$, this algorithm matches the
lower bound. For the PAC total ranking problem, we derive a tight lower bound,
and propose an algorithm that matches the lower bound. When $l=2$, the MNL
model reduces to the popular Plackett-Luce (PL) model. In this setting, our
results still outperform the state-of-the-art both theoretically and
numerically. We also compare our algorithms with the state-of-the-art using
synthetic data as well as real-world data to verify the efficiency of our
algorithms.
Message Passing Stein Variational Gradient Descent
Authors: Jingwei Zhuo, Chang Liu, Jiaxin Shi, Jun Zhu, Ning Chen, Bo Zhang
Date: 2017-11-13 00:00:00
Proceeding: ICML 2018 7
Tasks: Bayesian Inference, Variational Inference

Stein variational gradient descent (SVGD) is a recently proposed
particle-based Bayesian inference method, which has attracted a lot of interest
due to its remarkable approximation ability and particle efficiency compared to
traditional variational inference and Markov Chain Monte Carlo methods.
However, we observed that particles of SVGD tend to collapse to modes of the
target distribution, and this particle degeneracy phenomenon becomes more
severe with higher dimensions. Our theoretical analysis finds out that there
exists a negative correlation between the dimensionality and the repulsive
force of SVGD which should be blamed for this phenomenon. We propose Message
Passing SVGD (MP-SVGD) to solve this problem. By leveraging the conditional
independence structure of probabilistic graphical models (PGMs), MP-SVGD
converts the original high-dimensional global inference problem into a set of
local ones over the Markov blanket with lower dimensions. Experimental results
show its advantages of preventing vanishing repulsive force in high-dimensional
space over SVGD, and its particle efficiency and approximation flexibility over
other inference methods on graphical models.
Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search
Authors: Xinye Cai, Haoran Sun, Chunyang Zhu, Zhenyu Li, Qingfu Zhang
Date: 2018-06-08 00:00:00

In this paper, an evolutionary many-objective optimization algorithm based on
corner solution search (MaOEA-CS) was proposed. MaOEA-CS implicitly contains
two phases: the exploitative search for the most important boundary optimal
solutions - corner solutions, at the first phase, and the use of angle-based
selection [1] with the explorative search for the extension of PF approximation
at the second phase. Due to its high efficiency and robustness to the shapes of
PFs, it has won the CEC'2017 Competition on Evolutionary Many-Objective
Optimization. In addition, MaOEA-CS has also been applied on two real-world
engineering optimization problems with very irregular PFs. The experimental
results show that MaOEA-CS outperforms other six state-of-the-art compared
algorithms, which indicates it has the ability to handle real-world complex
optimization problems with irregular PFs.
SGD and Hogwild! Convergence Without the Bounded Gradients Assumption
Authors: Lam M. Nguyen, Phuong Ha Nguyen, Marten van Dijk, Peter Richtárik, Katya Scheinberg, Martin Takáč
Date: 2018-02-11 00:00:00
Proceeding: ICML 2018 7
Tasks: BIG-bench Machine Learning

Stochastic gradient descent (SGD) is the optimization algorithm of choice in
many machine learning applications such as regularized empirical risk
minimization and training deep neural networks. The classical convergence
analysis of SGD is carried out under the assumption that the norm of the
stochastic gradient is uniformly bounded. While this might hold for some loss
functions, it is always violated for cases where the objective function is
strongly convex. In (Bottou et al.,2016), a new analysis of convergence of SGD
is performed under the assumption that stochastic gradients are bounded with
respect to the true gradient norm. Here we show that for stochastic problems
arising in machine learning such bound always holds; and we also propose an
alternative convergence analysis of SGD with diminishing learning rate regime,
which results in more relaxed conditions than those in (Bottou et al.,2016). We
then move on the asynchronous parallel setting, and prove convergence of
Hogwild! algorithm in the same regime, obtaining the first convergence results
for this method in the case of diminished learning rate.
BSN: Boundary Sensitive Network for Temporal Action Proposal Generation
Authors: Tianwei Lin, Xu Zhao, Haisheng Su, Chongjing Wang, Ming Yang
Date: 2018-06-08 00:00:00
Proceeding: ECCV 2018 9
Tasks: Action Detection, Temporal Action Localization, Temporal Action Proposal Generation

Temporal action proposal generation is an important yet challenging problem,
since temporal proposals with rich action content are indispensable for
analysing real-world videos with long duration and high proportion irrelevant
content. This problem requires methods not only generating proposals with
precise temporal boundaries, but also retrieving proposals to cover truth
action instances with high recall and high overlap using relatively fewer
proposals. To address these difficulties, we introduce an effective proposal
generation method, named Boundary-Sensitive Network (BSN), which adopts "local
to global" fashion. Locally, BSN first locates temporal boundaries with high
probabilities, then directly combines these boundaries as proposals. Globally,
with Boundary-Sensitive Proposal feature, BSN retrieves proposals by evaluating
the confidence of whether a proposal contains an action within its region. We
conduct experiments on two challenging datasets: ActivityNet-1.3 and THUMOS14,
where BSN outperforms other state-of-the-art temporal action proposal
generation methods with high recall and high temporal precision. Finally,
further experiments demonstrate that by combining existing action classifiers,
our method significantly improves the state-of-the-art temporal action
detection performance.
Revisiting the poverty of the stimulus: hierarchical generalization without a hierarchical bias in recurrent neural networks
Authors: R. Thomas McCoy, Robert Frank, Tal Linzen
Date: 2018-02-25 00:00:00
Tasks: Sentence

Syntactic rules in natural language typically need to make reference to
hierarchical sentence structure. However, the simple examples that language
learners receive are often equally compatible with linear rules. Children
consistently ignore these linear explanations and settle instead on the correct
hierarchical one. This fact has motivated the proposal that the learner's
hypothesis space is constrained to include only hierarchical rules. We examine
this proposal using recurrent neural networks (RNNs), which are not constrained
in such a way. We simulate the acquisition of question formation, a
hierarchical transformation, in a fragment of English. We find that some RNN
architectures tend to learn the hierarchical rule, suggesting that hierarchical
cues within the language, combined with the implicit architectural biases
inherent in certain RNNs, may be sufficient to induce hierarchical
generalizations. The likelihood of acquiring the hierarchical generalization
increased when the language included an additional cue to hierarchy in the form
of subject-verb agreement, underscoring the role of cues to hierarchy in the
learner's input.
Lipschitz Continuity in Model-based Reinforcement Learning
Authors: Kavosh Asadi, Dipendra Misra, Michael L. Littman
Date: 2018-04-19 00:00:00
Proceeding: ICML 2018 7
Tasks: model, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

We examine the impact of learning Lipschitz continuous models in the context
of model-based reinforcement learning. We provide a novel bound on multi-step
prediction error of Lipschitz models where we quantify the error using the
Wasserstein metric. We go on to prove an error bound for the value-function
estimate arising from Lipschitz models and show that the estimated value
function is itself Lipschitz. We conclude with empirical results that show the
benefits of controlling the Lipschitz constant of neural-network models.
Representation Learning of Entities and Documents from Knowledge Base Descriptions
Authors: Ikuya Yamada, Hiroyuki Shindo, Yoshiyasu Takefuji
Date: 2018-06-08 00:00:00
Proceeding: COLING 2018 8
Tasks: Entity Typing, General Classification, Representation Learning, text-classification, Text Classification

In this paper, we describe TextEnt, a neural network model that learns
distributed representations of entities and documents directly from a knowledge
base (KB). Given a document in a KB consisting of words and entity annotations,
we train our model to predict the entity that the document describes and map
the document and its target entity close to each other in a continuous vector
space. Our model is trained using a large number of documents extracted from
Wikipedia. The performance of the proposed model is evaluated using two tasks,
namely fine-grained entity typing and multiclass text classification. The
results demonstrate that our model achieves state-of-the-art performance on
both tasks. The code and the trained representations are made available online
for further academic research.
A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations
Authors: Weili Nie, Yang Zhang, Ankit Patel
Date: 2018-05-18 00:00:00
Proceeding: ICML 2018 7

Backpropagation-based visualizations have been proposed to interpret convolutional neural networks (CNNs), however a theory is missing to justify their behaviors: Guided backpropagation (GBP) and deconvolutional network (DeconvNet) generate more human-interpretable but less class-sensitive visualizations than saliency map. Motivated by this, we develop a theoretical explanation revealing that GBP and DeconvNet are essentially doing (partial) image recovery which is unrelated to the network decisions. Specifically, our analysis shows that the backward ReLU introduced by GBP and DeconvNet, and the local connections in CNNs are the two main causes of compelling visualizations. Extensive experiments are provided that support the theoretical analysis.
Efficient Full-Matrix Adaptive Regularization
Authors: Naman Agarwal, Brian Bullins, Xinyi Chen, Elad Hazan, Karan Singh, Cyril Zhang, Yi Zhang
Date: 2018-06-08 00:00:00
Proceeding: ICLR 2019 5

Adaptive regularization methods pre-multiply a descent direction by a preconditioning matrix. Due to the large number of parameters of machine learning problems, full-matrix preconditioning methods are prohibitively expensive. We show how to modify full-matrix adaptive regularization in order to make it practical and effective. We also provide a novel theoretical analysis for adaptive regularization in non-convex optimization settings. The core of our algorithm, termed GGT, consists of the efficient computation of the inverse square root of a low-rank matrix. Our preliminary experiments show improved iteration-wise convergence rates across synthetic tasks and standard deep learning benchmarks, and that the more carefully-preconditioned steps sometimes lead to a better solution.
Dank Learning: Generating Memes Using Deep Neural Networks
Authors: Abel L Peirson V, E Meltem Tolunay
Date: 2018-06-08 00:00:00
Tasks: Diversity

We introduce a novel meme generation system, which given any image can
produce a humorous and relevant caption. Furthermore, the system can be
conditioned on not only an image but also a user-defined label relating to the
meme template, giving a handle to the user on meme content. The system uses a
pretrained Inception-v3 network to return an image embedding which is passed to
an attention-based deep-layer LSTM model producing the caption - inspired by
the widely recognised Show and Tell Model. We implement a modified beam search
to encourage diversity in the captions. We evaluate the quality of our model
using perplexity and human assessment on both the quality of memes generated
and whether they can be differentiated from real ones. Our model produces
original memes that cannot on the whole be differentiated from real ones.
A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations
Authors: Mohammad Amin Nabian, Hadi Meidani
Date: 2018-06-08 00:00:00

Developing efficient numerical algorithms for the solution of high
dimensional random Partial Differential Equations (PDEs) has been a challenging
task due to the well-known curse of dimensionality. We present a new solution
framework for these problems based on a deep learning approach. Specifically,
the random PDE is approximated by a feed-forward fully-connected deep residual
network, with either strong or weak enforcement of initial and boundary
constraints. The framework is mesh-free, and can handle irregular computational
domains. Parameters of the approximating deep neural network are determined
iteratively using variants of the Stochastic Gradient Descent (SGD) algorithm.
The satisfactory accuracy of the proposed frameworks is numerically
demonstrated on diffusion and heat conduction problems, in comparison with the
converged Monte Carlo-based finite element results.
Endoscopic navigation in the absence of CT imaging
Authors: Ayushi Sinha, Xingtong Liu, Austin Reiter, Masaru Ishii, Gregory D. Hager, Russell H. Taylor
Date: 2018-06-08 00:00:00
Tasks: Computed Tomography (CT)

Clinical examinations that involve endoscopic exploration of the nasal cavity
and sinuses often do not have a reference image to provide structural context
to the clinician. In this paper, we present a system for navigation during
clinical endoscopic exploration in the absence of computed tomography (CT)
scans by making use of shape statistics from past CT scans. Using a deformable
registration algorithm along with dense reconstructions from video, we show
that we are able to achieve submillimeter registrations in in-vivo clinical
data and are able to assign confidence to these registrations using confidence
criteria established using simulated data.
Using Social Network Information in Bayesian Truth Discovery
Authors: Jielong Yang, Junshan Wang, Wee Peng Tay
Date: 2018-06-08 00:00:00
Tasks: Variational Inference

We investigate the problem of truth discovery based on opinions from multiple
agents who may be unreliable or biased. We consider the case where agents'
reliabilities or biases are correlated if they belong to the same community,
which defines a group of agents with similar opinions regarding a particular
event. An agent can belong to different communities for different events, and
these communities are unknown a priori. We incorporate knowledge of the agents'
social network in our truth discovery framework and develop Laplace variational
inference methods to estimate agents' reliabilities, communities, and the event
states. We also develop a stochastic variational inference method to scale our
model to large social networks. Simulations and experiments on real data
suggest that when observations are sparse, our proposed methods perform better
than several other inference methods, including majority voting, TruthFinder,
AccuSim, the Confidence-Aware Truth Discovery method, the Bayesian Classifier
Combination (BCC) method, and the Community BCC method.