pwc-archive/papers-with-abstracts huggingface dataset:
Statistical Learnability of Generalized Additive Models based on Total Variation Regularization
Authors: Shin Matsushima
Date: 2018-02-08 00:00:00
Tasks: Additive models, General Classification

A generalized additive model (GAM, Hastie and Tibshirani (1987)) is a
nonparametric model by the sum of univariate functions with respect to each
explanatory variable, i.e., $f({\mathbf x}) = \sum f_j(x_j)$, where
$x_j\in\mathbb{R}$ is $j$-th component of a sample ${\mathbf x}\in
\mathbb{R}^p$. In this paper, we introduce the total variation (TV) of a
function as a measure of the complexity of functions in $L^1_{\rm
c}(\mathbb{R})$-space. Our analysis shows that a GAM based on TV-regularization
exhibits a Rademacher complexity of $O(\sqrt{\frac{\log p}{m}})$, which is
tight in terms of both $m$ and $p$ in the agnostic case of the classification
problem. In result, we obtain generalization error bounds for finite samples
according to work by Bartlett and Mandelson (2002).
Online Continuous Submodular Maximization
Authors: Lin Chen, Hamed Hassani, Amin Karbasi
Date: 2018-02-16 00:00:00

In this paper, we consider an online optimization process, where the
objective functions are not convex (nor concave) but instead belong to a broad
class of continuous submodular functions. We first propose a variant of the
Frank-Wolfe algorithm that has access to the full gradient of the objective
functions. We show that it achieves a regret bound of $O(\sqrt{T})$ (where $T$
is the horizon of the online optimization problem) against a
$(1-1/e)$-approximation to the best feasible solution in hindsight. However, in
many scenarios, only an unbiased estimate of the gradients are available. For
such settings, we then propose an online stochastic gradient ascent algorithm
that also achieves a regret bound of $O(\sqrt{T})$ regret, albeit against a
weaker $1/2$-approximation to the best feasible solution in hindsight. We also
generalize our results to $\gamma$-weakly submodular functions and prove the
same sublinear regret bounds. Finally, we demonstrate the efficiency of our
algorithms on a few problem instances, including non-convex/non-concave
quadratic programs, multilinear extensions of submodular set functions, and
D-optimal design.
Jointly Learning to Construct and Control Agents using Deep Reinforcement Learning
Authors: Charles Schaff, David Yunis, Ayan Chakrabarti, Matthew R. Walter
Date: 2018-01-04 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The physical design of a robot and the policy that controls its motion are
inherently coupled, and should be determined according to the task and
environment. In an increasing number of applications, data-driven and
learning-based approaches, such as deep reinforcement learning, have proven
effective at designing control policies. For most tasks, the only way to
evaluate a physical design with respect to such control policies is
empirical--i.e., by picking a design and training a control policy for it.
Since training these policies is time-consuming, it is computationally
infeasible to train separate policies for all possible designs as a means to
identify the best one. In this work, we address this limitation by introducing
a method that performs simultaneous joint optimization of the physical design
and control network. Our approach maintains a distribution over designs and
uses reinforcement learning to optimize a control policy to maximize expected
reward over the design distribution. We give the controller access to design
parameters to allow it to tailor its policy to each design in the distribution.
Throughout training, we shift the distribution towards higher-performing
designs, eventually converging to a design and control policy that are jointly
optimal. We evaluate our approach in the context of legged locomotion, and
demonstrate that it discovers novel designs and walking gaits, outperforming
baselines in both performance and efficiency.
Fluency Over Adequacy: A Pilot Study in Measuring User Trust in Imperfect MT
Authors: Marianna J. Martindale, Marine Carpuat
Date: 2018-02-16 00:00:00
Proceeding: WS 2018 3
Tasks: Machine Translation, Translation

Although measuring intrinsic quality has been a key factor in the advancement
of Machine Translation (MT), successfully deploying MT requires considering not
just intrinsic quality but also the user experience, including aspects such as
trust. This work introduces a method of studying how users modulate their trust
in an MT system after seeing errorful (disfluent or inadequate) output amidst
good (fluent and adequate) output. We conduct a survey to determine how users
respond to good translations compared to translations that are either adequate
but not fluent, or fluent but not adequate. In this pilot study, users
responded strongly to disfluent translations, but were, surprisingly, much less
concerned with adequacy.
Policy Evaluation and Optimization with Continuous Treatments
Authors: Nathan Kallus, Angela Zhou
Date: 2018-02-16 00:00:00

We study the problem of policy evaluation and learning from batched
contextual bandit data when treatments are continuous, going beyond previous
work on discrete treatments. Previous work for discrete treatment/action spaces
focuses on inverse probability weighting (IPW) and doubly robust (DR) methods
that use a rejection sampling approach for evaluation and the equivalent
weighted classification problem for learning. In the continuous setting, this
reduction fails as we would almost surely reject all observations. To tackle
the case of continuous treatments, we extend the IPW and DR approaches to the
continuous setting using a kernel function that leverages treatment proximity
to attenuate discrete rejection. Our policy estimator is consistent and we
characterize the optimal bandwidth. The resulting continuous policy optimizer
(CPO) approach using our estimator achieves convergent regret and approaches
the best-in-class policy for learnable policy classes. We demonstrate that the
estimator performs well and, in particular, outperforms a discretization-based
benchmark. We further study the performance of our policy optimizer in a case
study on personalized dosing based on a dataset of Warfarin patients, their
covariates, and final therapeutic doses. Our learned policy outperforms
benchmarks and nears the oracle-best linear policy.
Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis
Authors: Pengtao Xie, Wei Wu, Yichen Zhu, Eric P. Xing
Date: 2018-02-16 00:00:00
Proceeding: ICML 2018 7
Tasks: Metric Learning

Distance metric learning (DML), which learns a distance metric from labeled
"similar" and "dissimilar" data pairs, is widely utilized. Recently, several
works investigate orthogonality-promoting regularization (OPR), which
encourages the projection vectors in DML to be close to being orthogonal, to
achieve three effects: (1) high balancedness -- achieving comparable
performance on both frequent and infrequent classes; (2) high compactness --
using a small number of projection vectors to achieve a "good" metric; (3) good
generalizability -- alleviating overfitting to training data. While showing
promising results, these approaches suffer three problems. First, they involve
solving non-convex optimization problems where achieving the global optimal is
NP-hard. Second, it lacks a theoretical understanding why OPR can lead to
balancedness. Third, the current generalization error analysis of OPR is not
directly on the regularizer. In this paper, we address these three issues by
(1) seeking convex relaxations of the original nonconvex problems so that the
global optimal is guaranteed to be achievable; (2) providing a formal analysis
on OPR's capability of promoting balancedness; (3) providing a theoretical
analysis that directly reveals the relationship between OPR and generalization
performance. Experiments on various datasets demonstrate that our convex
methods are more effective in promoting balancedness, compactness, and
generalization, and are computationally more efficient, compared with the
nonconvex methods.
WebEye - Automated Collection of Malicious HTTP Traffic
Authors: Johann Vierthaler, Roman Kruszelnicki, Julian Schütte
Date: 2018-02-16 00:00:00
Tasks: BIG-bench Machine Learning, Malware Detection

With malware detection techniques increasingly adopting machine learning
approaches, the creation of precise training sets becomes more and more
important. Large data sets of realistic web traffic, correctly classified as
benign or malicious are needed, not only to train classic and deep learning
algorithms, but also to serve as evaluation benchmarks for existing malware
detection products. Interestingly, despite the vast number and versatility of
threats a user may encounter when browsing the web, actual malicious content is
often hard to come by, since prerequisites such as browser and operating system
type and version must be met in order to receive the payload from a malware
distributing server. In combination with privacy constraints on data sets of
actual user traffic, it is difficult for researchers and product developers to
evaluate anti-malware solutions against large-scale data sets of realistic web
traffic. In this paper we present WebEye, a framework that autonomously creates
realistic HTTP traffic, enriches recorded traffic with additional information,
and classifies records as malicious or benign, using different classifiers. We
are using WebEye to collect malicious HTML and JavaScript and show how datasets
created with WebEye can be used to train machine learning based malware
detection algorithms. We regard WebEye and the data sets it creates as a tool
for researchers and product developers to evaluate and improve their AI-based
anti-malware solutions against large-scale benchmarks.
Does William Shakespeare REALLY Write Hamlet? Knowledge Representation Learning with Confidence
Authors: Ruobing Xie, Zhiyuan Liu, Fen Lin, Leyu Lin
Date: 2017-05-09 00:00:00
Tasks: Knowledge Graph Completion, Knowledge Graphs, Representation Learning, Triple Classification

Knowledge graphs (KGs), which could provide essential relational information
between entities, have been widely utilized in various knowledge-driven
applications. Since the overall human knowledge is innumerable that still grows
explosively and changes frequently, knowledge construction and update
inevitably involve automatic mechanisms with less human supervision, which
usually bring in plenty of noises and conflicts to KGs. However, most
conventional knowledge representation learning methods assume that all triple
facts in existing KGs share the same significance without any noises. To
address this problem, we propose a novel confidence-aware knowledge
representation learning framework (CKRL), which detects possible noises in KGs
while learning knowledge representations with confidence simultaneously.
Specifically, we introduce the triple confidence to conventional
translation-based methods for knowledge representation learning. To make triple
confidence more flexible and universal, we only utilize the internal structural
information in KGs, and propose three kinds of triple confidences considering
both local and global structural information. In experiments, We evaluate our
models on knowledge graph noise detection, knowledge graph completion and
triple classification. Experimental results demonstrate that our
confidence-aware models achieve significant and consistent improvements on all
tasks, which confirms the capability of CKRL modeling confidence with
structural information in both KG noise detection and knowledge representation
learning.
Dropout Model Evaluation in MOOCs
Authors: Josh Gardner, Christopher Brooks
Date: 2018-02-16 00:00:00
Tasks: model

The field of learning analytics needs to adopt a more rigorous approach for
predictive model evaluation that matches the complex practice of
model-building. In this work, we present a procedure to statistically test
hypotheses about model performance which goes beyond the state-of-the-practice
in the community to analyze both algorithms and feature extraction methods from
raw data. We apply this method to a series of algorithms and feature sets
derived from a large sample of Massive Open Online Courses (MOOCs). While a
complete comparison of all potential modeling approaches is beyond the scope of
this paper, we show that this approach reveals a large gap in dropout
prediction performance between forum-, assignment-, and clickstream-based
feature extraction methods, where the latter is significantly better than the
former two, which are in turn indistinguishable from one another. This work has
methodological implications for evaluating predictive or AI-based models of
student success, and practical implications for the design and targeting of
at-risk student models and interventions.
Abductive reasoning as the basis to reproduce expert criteria in ECG Atrial Fibrillation identification
Authors: Tomás Teijeiro, Constantino A. García, Daniel Castro, Paulo Félix
Date: 2018-02-16 00:00:00
Tasks: General Classification, Rhythm

Objective: This work aims at providing a new method for the automatic
detection of atrial fibrillation, other arrhythmia and noise on short single
lead ECG signals, emphasizing the importance of the interpretability of the
classification results.
  Approach: A morphological and rhythm description of the cardiac behavior is
obtained by a knowledge-based interpretation of the signal using the
\textit{Construe} abductive framework. Then, a set of meaningful features are
extracted for each individual heartbeat and as a summary of the full record.
The feature distributions were used to elucidate the expert criteria underlying
the labeling of the 2017 Physionet/CinC Challenge dataset, enabling a manual
partial relabeling to improve the consistency of the classification rules.
Finally, state-of-the-art machine learning methods are combined to provide an
answer on the basis of the feature values.
  Main results: The proposal tied for the first place in the official stage of
the Challenge, with a combined $F_1$ score of 0.83, and was even improved in
the follow-up stage to 0.85 with a significant simplification of the model.
  Significance: This approach demonstrates the potential of \textit{Construe}
to provide robust and valuable descriptions of temporal data even with
significant amounts of noise and artifacts. Also, we discuss the importance of
a consistent classification criteria in manually labeled training datasets, and
the fundamental advantages of knowledge-based approaches to formalize and
validate that criteria.
Improved GQ-CNN: Deep Learning Model for Planning Robust Grasps
Authors: Maciej Jaśkowski, Jakub Świątkowski, Michał Zając, Maciej Klimek, Jarek Potiuk, Piotr Rybicki, Piotr Polatowski, Przemysław Walczyk, Kacper Nowicki, Marek Cygan
Date: 2018-02-16 00:00:00
Tasks: Deep Learning

Recent developments in the field of robot grasping have shown great
improvements in the grasp success rates when dealing with unknown objects. In
this work we improve on one of the most promising approaches, the Grasp Quality
Convolutional Neural Network (GQ-CNN) trained on the DexNet 2.0 dataset. We
propose a new architecture for the GQ-CNN and describe practical improvements
that increase the model validation accuracy from 92.2% to 95.8% and from 85.9%
to 88.0% on respectively image-wise and object-wise training and validation
splits.
WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models
Authors: Marine Le Morvan, Jean-Philippe Vert
Date: 2018-02-16 00:00:00
Proceeding: ICML 2018 7

Learning sparse linear models with two-way interactions is desirable in many
application domains such as genomics. l1-regularised linear models are popular
to estimate sparse models, yet standard implementations fail to address
specifically the quadratic explosion of candidate two-way interactions in high
dimensions, and typically do not scale to genetic data with hundreds of
thousands of features. Here we present WHInter, a working set algorithm to
solve large l1-regularised problems with two-way interactions for binary design
matrices. The novelty of WHInter stems from a new bound to efficiently identify
working sets while avoiding to scan all features, and on fast computations
inspired from solutions to the maximum inner product search problem. We apply
WHInter to simulated and real genetic data and show that it is more scalable
and two orders of magnitude faster than the state of the art.
Variance-Reduced Stochastic Learning under Random Reshuffling
Authors: Bicheng Ying, Kun Yuan, Ali H. Sayed
Date: 2017-08-04 00:00:00

Several useful variance-reduced stochastic gradient algorithms, such as SVRG,
SAGA, Finito, and SAG, have been proposed to minimize empirical risks with
linear convergence properties to the exact minimizer. The existing convergence
results assume uniform data sampling with replacement. However, it has been
observed in related works that random reshuffling can deliver superior
performance over uniform sampling and, yet, no formal proofs or guarantees of
exact convergence exist for variance-reduced algorithms under random
reshuffling. This paper makes two contributions. First, it resolves this open
issue and provides the first theoretical guarantee of linear convergence under
random reshuffling for SAGA; the argument is also adaptable to other
variance-reduced algorithms. Second, under random reshuffling, the paper
proposes a new amortized variance-reduced gradient (AVRG) algorithm with
constant storage requirements compared to SAGA and with balanced gradient
computations compared to SVRG. AVRG is also shown analytically to converge
linearly.
Spectral Normalization for Generative Adversarial Networks
Authors: Takeru Miyato, Toshiki Kataoka, Masanori Koyama, Yuichi Yoshida
Date: 2018-02-16 00:00:00
Proceeding: ICLR 2018 1
Tasks: Image Generation

One of the challenges in the study of generative adversarial networks is the
instability of its training. In this paper, we propose a novel weight
normalization technique called spectral normalization to stabilize the training
of the discriminator. Our new normalization technique is computationally light
and easy to incorporate into existing implementations. We tested the efficacy
of spectral normalization on CIFAR10, STL-10, and ILSVRC2012 dataset, and we
experimentally confirmed that spectrally normalized GANs (SN-GANs) is capable
of generating images of better or equal quality relative to the previous
training stabilization techniques.
Decision-Based Adversarial Attacks: Reliable Attacks Against Black-Box Machine Learning Models
Authors: Wieland Brendel, Jonas Rauber, Matthias Bethge
Date: 2017-12-12 00:00:00
Proceeding: ICLR 2018 1
Tasks: BIG-bench Machine Learning

Many machine learning algorithms are vulnerable to almost imperceptible
perturbations of their inputs. So far it was unclear how much risk adversarial
perturbations carry for the safety of real-world machine learning applications
because most methods used to generate such perturbations rely either on
detailed model information (gradient-based attacks) or on confidence scores
such as class probabilities (score-based attacks), neither of which are
available in most real-world scenarios. In many such cases one currently needs
to retreat to transfer-based attacks which rely on cumbersome substitute
models, need access to the training data and can be defended against. Here we
emphasise the importance of attacks which solely rely on the final model
decision. Such decision-based attacks are (1) applicable to real-world
black-box models such as autonomous cars, (2) need less knowledge and are
easier to apply than transfer-based attacks and (3) are more robust to simple
defences than gradient- or score-based attacks. Previous attacks in this
category were limited to simple models or simple datasets. Here we introduce
the Boundary Attack, a decision-based attack that starts from a large
adversarial perturbation and then seeks to reduce the perturbation while
staying adversarial. The attack is conceptually simple, requires close to no
hyperparameter tuning, does not rely on substitute models and is competitive
with the best gradient-based attacks in standard computer vision tasks like
ImageNet. We apply the attack on two black-box algorithms from Clarifai.com.
The Boundary Attack in particular and the class of decision-based attacks in
general open new avenues to study the robustness of machine learning models and
raise new questions regarding the safety of deployed machine learning systems.
An implementation of the attack is available as part of Foolbox at
https://github.com/bethgelab/foolbox .
Improving Retrieval Modeling Using Cross Convolution Networks And Multi Frequency Word Embedding
Authors: Guozhen An, Mehrnoosh Shafiee, Davood Shamsi
Date: 2018-02-15 00:00:00
Tasks: Chatbot, Retrieval

To build a satisfying chatbot that has the ability of managing a
goal-oriented multi-turn dialogue, accurate modeling of human conversation is
crucial. In this paper we concentrate on the task of response selection for
multi-turn human-computer conversation with a given context. Previous
approaches show weakness in capturing information of rare keywords that appear
in either or both context and correct response, and struggle with long input
sequences. We propose Cross Convolution Network (CCN) and Multi Frequency word
embedding to address both problems. We train several models using the Ubuntu
Dialogue dataset which is the largest freely available multi-turn based
dialogue corpus. We further build an ensemble model by averaging predictions of
multiple models. We achieve a new state-of-the-art on this dataset with
considerable improvements compared to previous best results.
Supervising Unsupervised Learning
Authors: Vikas K. Garg, Adam Kalai
Date: 2017-09-14 00:00:00
Proceeding: NeurIPS 2018
Tasks: Clustering, Zero-Shot Learning

We introduce a framework to leverage knowledge acquired from a repository of
(heterogeneous) supervised datasets to new unsupervised datasets. Our
perspective avoids the subjectivity inherent in unsupervised learning by
reducing it to supervised learning, and provides a principled way to evaluate
unsupervised algorithms. We demonstrate the versatility of our framework via
simple agnostic bounds on unsupervised problems. In the context of clustering,
our approach helps choose the number of clusters and the clustering algorithm,
remove the outliers, and provably circumvent the Kleinberg's impossibility
result. Experimental results across hundreds of problems demonstrate improved
performance on unsupervised data with simple algorithms, despite the fact that
our problems come from heterogeneous domains. Additionally, our framework lets
us leverage deep networks to learn common features from many such small
datasets, and perform zero shot learning.