pwc-archive/papers-with-abstracts huggingface dataset:
Learning with Abandonment
Authors: Ramesh Johari, Sven Schmit
Date: 2018-02-23 00:00:00
Proceeding: ICML 2018 7

Consider a platform that wants to learn a personalized policy for each user,
but the platform faces the risk of a user abandoning the platform if she is
dissatisfied with the actions of the platform. For example, a platform is
interested in personalizing the number of newsletters it sends, but faces the
risk that the user unsubscribes forever. We propose a general thresholded
learning model for scenarios like this, and discuss the structure of optimal
policies. We describe salient features of optimal personalization algorithms
and how feedback the platform receives impacts the results. Furthermore, we
investigate how the platform can efficiently learn the heterogeneity across
users by interacting with a population and provide performance guarantees.
Twin Networks: Matching the Future for Sequence Generation
Authors: Dmitriy Serdyuk, Nan Rosemary Ke, Alessandro Sordoni, Adam Trischler, Chris Pal, Yoshua Bengio
Date: 2017-08-22 00:00:00
Proceeding: ICLR 2018 1
Tasks: Caption Generation, speech-recognition, Speech Recognition

We propose a simple technique for encouraging generative RNNs to plan ahead.
We train a "backward" recurrent network to generate a given sequence in reverse
order, and we encourage states of the forward model to predict cotemporal
states of the backward model. The backward network is used only during
training, and plays no role during sampling or inference. We hypothesize that
our approach eases modeling of long-term dependencies by implicitly forcing the
forward states to hold information about the longer-term future (as contained
in the backward states). We show empirically that our approach achieves 9%
relative improvement for a speech recognition task, and achieves significant
improvement on a COCO caption generation task.
Training Confidence-calibrated Classifiers for Detecting Out-of-Distribution Samples
Authors: Kimin Lee, Honglak Lee, Kibok Lee, Jinwoo Shin
Date: 2017-11-26 00:00:00
Proceeding: ICLR 2018 1

The problem of detecting whether a test sample is from in-distribution (i.e.,
training distribution by a classifier) or out-of-distribution sufficiently
different from it arises in many real-world machine learning applications.
However, the state-of-art deep neural networks are known to be highly
overconfident in their predictions, i.e., do not distinguish in- and
out-of-distributions. Recently, to handle this issue, several threshold-based
detectors have been proposed given pre-trained neural classifiers. However, the
performance of prior works highly depends on how to train the classifiers since
they only focus on improving inference procedures. In this paper, we develop a
novel training method for classifiers so that such inference algorithms can
work better. In particular, we suggest two additional terms added to the
original loss (e.g., cross entropy). The first one forces samples from
out-of-distribution less confident by the classifier and the second one is for
(implicitly) generating most effective training samples for the first one. In
essence, our method jointly trains both classification and generative neural
networks for out-of-distribution. We demonstrate its effectiveness using deep
convolutional neural networks on various popular image datasets.
PDDLStream: Integrating Symbolic Planners and Blackbox Samplers via Optimistic Adaptive Planning
Authors: Caelan Reed Garrett, Tomás Lozano-Pérez, Leslie Pack Kaelbling
Date: 2018-02-23 00:00:00
Tasks: Motion Planning

Many planning applications involve complex relationships defined on high-dimensional, continuous variables. For example, robotic manipulation requires planning with kinematic, collision, visibility, and motion constraints involving robot configurations, object poses, and robot trajectories. These constraints typically require specialized procedures to sample satisfying values. We extend PDDL to support a generic, declarative specification for these procedures that treats their implementation as black boxes. We provide domain-independent algorithms that reduce PDDLStream problems to a sequence of finite PDDL problems. We also introduce an algorithm that dynamically balances exploring new candidate plans and exploiting existing ones. This enables the algorithm to greedily search the space of parameter bindings to more quickly solve tightly-constrained problems as well as locally optimize to produce low-cost solutions. We evaluate our algorithms on three simulated robotic planning domains as well as several real-world robotic tasks.
Machine learning based hyperspectral image analysis: A survey
Authors: Utsav B. Gewali, Sildomar T. Monteiro, Eli Saber
Date: 2018-02-23 00:00:00
Tasks: BIG-bench Machine Learning, Clustering, Ensemble Learning, Gaussian Processes, Hyperspectral image analysis, Land Cover Classification, parameter estimation, regression, Survey

Hyperspectral sensors enable the study of the chemical properties of scene
materials remotely for the purpose of identification, detection, and chemical
composition analysis of objects in the environment. Hence, hyperspectral images
captured from earth observing satellites and aircraft have been increasingly
important in agriculture, environmental monitoring, urban planning, mining, and
defense. Machine learning algorithms due to their outstanding predictive power
have become a key tool for modern hyperspectral image analysis. Therefore, a
solid understanding of machine learning techniques have become essential for
remote sensing researchers and practitioners. This paper reviews and compares
recent machine learning-based hyperspectral image analysis methods published in
literature. We organize the methods by the image analysis task and by the type
of machine learning algorithm, and present a two-way mapping between the image
analysis tasks and the types of machine learning algorithms that can be applied
to them. The paper is comprehensive in coverage of both hyperspectral image
analysis tasks and machine learning algorithms. The image analysis tasks
considered are land cover classification, target detection, unmixing, and
physical parameter estimation. The machine learning algorithms covered are
Gaussian models, linear regression, logistic regression, support vector
machines, Gaussian mixture model, latent linear models, sparse linear models,
Gaussian mixture models, ensemble learning, directed graphical models,
undirected graphical models, clustering, Gaussian processes, Dirichlet
processes, and deep learning. We also discuss the open challenges in the field
of hyperspectral image analysis and explore possible future directions.
"You are no Jack Kennedy": On Media Selection of Highlights from Presidential Debates
Authors: Chenhao Tan, Hao Peng, Noah A. Smith
Date: 2018-02-23 00:00:00
Tasks: Binary Classification

Political speeches and debates play an important role in shaping the images
of politicians, and the public often relies on media outlets to select bits of
political communication from a large pool of utterances. It is an important
research question to understand what factors impact this selection process.
  To quantitatively explore the selection process, we build a three- decade
dataset of presidential debate transcripts and post-debate coverage. We first
examine the effect of wording and propose a binary classification framework
that controls for both the speaker and the debate situation. We find that
crowdworkers can only achieve an accuracy of 60% in this task, indicating that
media choices are not entirely obvious. Our classifiers outperform crowdworkers
on average, mainly in primary debates. We also compare important factors from
crowdworkers' free-form explanations with those from data-driven methods and
find interesting differences. Few crowdworkers mentioned that "context
matters", whereas our data show that well-quoted sentences are more distinct
from the previous utterance by the same speaker than less-quoted sentences.
Finally, we examine the aggregate effect of media preferences towards different
wordings to understand the extent of fragmentation among media outlets. By
analyzing a bipartite graph built from quoting behavior in our data, we observe
a decreasing trend in bipartisan coverage.
Advantages of versatile neural-network decoding for topological codes
Authors: Nishad Maskara, Aleksander Kubica, Tomas Jochym-O'Connor
Date: 2018-02-23 00:00:00

Finding optimal correction of errors in generic stabilizer codes is a
computationally hard problem, even for simple noise models. While this task can
be simplified for codes with some structure, such as topological stabilizer
codes, developing good and efficient decoders still remains a challenge. In our
work, we systematically study a very versatile class of decoders based on
feedforward neural networks. To demonstrate adaptability, we apply neural
decoders to the triangular color and toric codes under various noise models
with realistic features, such as spatially-correlated errors. We report that
neural decoders provide significant improvement over leading efficient decoders
in terms of the error-correction threshold. Using neural networks simplifies
the process of designing well-performing decoders, and does not require prior
knowledge of the underlying noise model.
Learning Optimal Policies from Observational Data
Authors: Onur Atan, William R. Zame, M. van der Schaar
Date: 2018-02-23 00:00:00
Tasks: Domain Adaptation, Selection bias

Choosing optimal (or at least better) policies is an important problem in
domains from medicine to education to finance and many others. One approach to
this problem is through controlled experiments/trials - but controlled
experiments are expensive. Hence it is important to choose the best policies on
the basis of observational data. This presents two difficult challenges: (i)
missing counterfactuals, and (ii) selection bias. This paper presents
theoretical bounds on estimation errors of counterfactuals from observational
data by making connections to domain adaptation theory. It also presents a
principled way of choosing optimal policies using domain adversarial neural
networks. We illustrate the effectiveness of domain adversarial training
together with various features of our algorithm on a semi-synthetic breast
cancer dataset and a supervised UCI dataset (Statlog).
State-of-the-art Speech Recognition With Sequence-to-Sequence Models
Authors: Chung-Cheng Chiu, Tara N. Sainath, Yonghui Wu, Rohit Prabhavalkar, Patrick Nguyen, Zhifeng Chen, Anjuli Kannan, Ron J. Weiss, Kanishka Rao, Ekaterina Gonina, Navdeep Jaitly, Bo Li, Jan Chorowski, Michiel Bacchiani
Date: 2017-12-05 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Decoder, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

Attention-based encoder-decoder architectures such as Listen, Attend, and
Spell (LAS), subsume the acoustic, pronunciation and language model components
of a traditional automatic speech recognition (ASR) system into a single neural
network. In previous work, we have shown that such architectures are comparable
to state-of-theart ASR systems on dictation tasks, but it was not clear if such
architectures would be practical for more challenging tasks such as voice
search. In this work, we explore a variety of structural and optimization
improvements to our LAS model which significantly improve performance. On the
structural side, we show that word piece models can be used instead of
graphemes. We also introduce a multi-head attention architecture, which offers
improvements over the commonly-used single-head attention. On the optimization
side, we explore synchronous training, scheduled sampling, label smoothing, and
minimum word error rate optimization, which are all shown to improve accuracy.
We present results with a unidirectional LSTM encoder for streaming
recognition. On a 12, 500 hour voice search task, we find that the proposed
changes improve the WER from 9.2% to 5.6%, while the best conventional system
achieves 6.7%; on a dictation task our model achieves a WER of 4.1% compared to
5% for the conventional system.
An Algorithmic Framework to Control Bias in Bandit-based Personalization
Authors: L. Elisa Celis, Sayash Kapoor, Farnood Salehi, Nisheeth K. Vishnoi
Date: 2018-02-23 00:00:00
Tasks: Fairness

Personalization is pervasive in the online space as it leads to higher
efficiency and revenue by allowing the most relevant content to be served to
each user. However, recent studies suggest that personalization methods can
propagate societal or systemic biases and polarize opinions; this has led to
calls for regulatory mechanisms and algorithms to combat bias and inequality.
Algorithmically, bandit optimization has enjoyed great success in learning user
preferences and personalizing content or feeds accordingly. We propose an
algorithmic framework that allows for the possibility to control bias or
discrimination in such bandit-based personalization. Our model allows for the
specification of general fairness constraints on the sensitive types of the
content that can be displayed to a user. The challenge, however, is to come up
with a scalable and low regret algorithm for the constrained optimization
problem that arises. Our main technical contribution is a provably fast and
low-regret algorithm for the fairness-constrained bandit optimization problem.
Our proofs crucially leverage the special structure of our problem. Experiments
on synthetic and real-world data sets show that our algorithmic framework can
control bias with only a minor loss to revenue.
Subsampling for Ridge Regression via Regularized Volume Sampling
Authors: Michał Dereziński, Manfred K. Warmuth
Date: 2017-10-14 00:00:00
Tasks: regression

Given $n$ vectors $\mathbf{x}_i\in \mathbb{R}^d$, we want to fit a linear
regression model for noisy labels $y_i\in\mathbb{R}$. The ridge estimator is a
classical solution to this problem. However, when labels are expensive, we are
forced to select only a small subset of vectors $\mathbf{x}_i$ for which we
obtain the labels $y_i$. We propose a new procedure for selecting the subset of
vectors, such that the ridge estimator obtained from that subset offers strong
statistical guarantees in terms of the mean squared prediction error over the
entire dataset of $n$ labeled vectors. The number of labels needed is
proportional to the statistical dimension of the problem which is often much
smaller than $d$. Our method is an extension of a joint subsampling procedure
called volume sampling. A second major contribution is that we speed up volume
sampling so that it is essentially as efficient as leverage scores, which is
the main i.i.d. subsampling procedure for this task. Finally, we show
theoretically and experimentally that volume sampling has a clear advantage
over any i.i.d. sampling when labels are expensive.
Nonlinear Online Learning with Adaptive Nyström Approximation
Authors: Si Si, Sanjiv Kumar, Yang Li
Date: 2018-02-21 00:00:00

Use of nonlinear feature maps via kernel approximation has led to success in
many online learning tasks. As a popular kernel approximation method,
Nystr\"{o}m approximation, has been well investigated, and various landmark
points selection methods have been proposed to improve the approximation
quality. However, these improved Nystr\"{o}m methods cannot be directly applied
to the online learning setting as they need to access the entire dataset to
learn the landmark points, while we need to update model on-the-fly in the
online setting. To address this challenge, we propose Adaptive Nystr\"{o}m
approximation for solving nonlinear online learning problems. The key idea is
to adaptively modify the landmark points via online kmeans and adjust the model
accordingly via solving least square problem followed by a gradient descent
step. We show that the resulting algorithm outperforms state-of-the-art online
learning methods under the same budget.
Glimpse Clouds: Human Activity Recognition from Unstructured Feature Points
Authors: Fabien Baradel, Christian Wolf, Julien Mille, Graham W. Taylor
Date: 2018-02-22 00:00:00
Proceeding: CVPR 2018 6
Tasks: Action Recognition, Activity Prediction, Activity Recognition, Human Activity Recognition, Skeleton Based Action Recognition, Temporal Action Localization

We propose a method for human activity recognition from RGB data that does
not rely on any pose information during test time and does not explicitly
calculate pose information internally. Instead, a visual attention module
learns to predict glimpse sequences in each frame. These glimpses correspond to
interest points in the scene that are relevant to the classified activities. No
spatial coherence is forced on the glimpse locations, which gives the module
liberty to explore different points at each frame and better optimize the
process of scrutinizing visual information. Tracking and sequentially
integrating this kind of unstructured data is a challenge, which we address by
separating the set of glimpses from a set of recurrent tracking/recognition
workers. These workers receive glimpses, jointly performing subsequent motion
tracking and activity prediction. The glimpses are soft-assigned to the
workers, optimizing coherence of the assignments in space, time and feature
space using an external memory module. No hard decisions are taken, i.e. each
glimpse point is assigned to all existing workers, albeit with different
importance. Our methods outperform state-of-the-art methods on the largest
human activity recognition dataset available to-date; NTU RGB+D Dataset, and on
a smaller human action recognition dataset Northwestern-UCLA Multiview Action
3D Dataset. Our code is publicly available at
https://github.com/fabienbaradel/glimpse_clouds.
Learning Latent Permutations with Gumbel-Sinkhorn Networks
Authors: Gonzalo Mena, David Belanger, Scott Linderman, Jasper Snoek
Date: 2018-02-23 00:00:00
Proceeding: ICLR 2018 1

Permutations and matchings are core building blocks in a variety of latent
variable models, as they allow us to align, canonicalize, and sort data.
Learning in such models is difficult, however, because exact marginalization
over these combinatorial objects is intractable. In response, this paper
introduces a collection of new methods for end-to-end learning in such models
that approximate discrete maximum-weight matching using the continuous Sinkhorn
operator. Sinkhorn iteration is attractive because it functions as a simple,
easy-to-implement analog of the softmax operator. With this, we can define the
Gumbel-Sinkhorn method, an extension of the Gumbel-Softmax method (Jang et al.
2016, Maddison2016 et al. 2016) to distributions over latent matchings. We
demonstrate the effectiveness of our method by outperforming competitive
baselines on a range of qualitatively different tasks: sorting numbers, solving
jigsaw puzzles, and identifying neural signals in worms.
Mixed Precision Training of Convolutional Neural Networks using Integer Operations
Authors: Dipankar Das, Naveen Mellempudi, Dheevatsa Mudigere, Dhiraj Kalamkar, Sasikanth Avancha, Kunal Banerjee, Srinivas Sridharan, Karthik Vaidyanathan, Bharat Kaul, Evangelos Georganas, Alexander Heinecke, Pradeep Dubey, Jesus Corbal, Nikita Shustrov, Roma Dubtsov, Evarist Fomenko, Vadim Pirogov
Date: 2018-02-03 00:00:00
Proceeding: ICLR 2018 1

The state-of-the-art (SOTA) for mixed precision training is dominated by
variants of low precision floating point operations, and in particular, FP16
accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a
lot of research has also happened in the domain of low and mixed-precision
Integer training, these works either present results for non-SOTA networks (for
instance only AlexNet for ImageNet-1K), or relatively small datasets (like
CIFAR-10). In this work, we train state-of-the-art visual understanding neural
networks on the ImageNet-1K dataset, with Integer operations on General Purpose
(GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate
(FMA) operations which take two pairs of INT16 operands and accumulate results
into an INT32 output.We propose a shared exponent representation of tensors and
develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network
operations. The nuances of developing an efficient integer convolution kernel
is examined, including methods to handle overflow of the INT32 accumulator. We
implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and
these networks achieve or exceed SOTA accuracy within the same number of
iterations as their FP32 counterparts without any change in hyper-parameters
and with a 1.8X improvement in end-to-end training throughput. To the best of
our knowledge these results represent the first INT16 training results on GP
hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported
accuracy using half-precision
Batch-Expansion Training: An Efficient Optimization Framework
Authors: Michał Dereziński, Dhruv Mahajan, S. Sathiya Keerthi, S. V. N. Vishwanathan, Markus Weimer
Date: 2017-04-22 00:00:00

We propose Batch-Expansion Training (BET), a framework for running a batch
optimizer on a gradually expanding dataset. As opposed to stochastic
approaches, batches do not need to be resampled i.i.d. at every iteration, thus
making BET more resource efficient in a distributed setting, and when
disk-access is constrained. Moreover, BET can be easily paired with most batch
optimizers, does not require any parameter-tuning, and compares favorably to
existing stochastic and batch methods. We show that when the batch size grows
exponentially with the number of outer iterations, BET achieves optimal
$O(1/\epsilon)$ data-access convergence rate for strongly convex objectives.
Experiments in parallel and distributed settings show that BET performs better
than standard batch and stochastic approaches.
Comparative Analysis of Unsupervised Algorithms for Breast MRI Lesion Segmentation
Authors: Sulaiman Vesal, Nishant Ravikumar, Stephan Ellman, Andreas Maier
Date: 2018-02-23 00:00:00
Tasks: Clustering, Lesion Segmentation, Segmentation

Accurate segmentation of breast lesions is a crucial step in evaluating the
characteristics of tumors. However, this is a challenging task, since breast
lesions have sophisticated shape, topological structure, and variation in the
intensity distribution. In this paper, we evaluated the performance of three
unsupervised algorithms for the task of breast Magnetic Resonance (MRI) lesion
segmentation, namely, Gaussian Mixture Model clustering, K-means clustering and
a marker-controlled Watershed transformation based method. All methods were
applied on breast MRI slices following selection of regions of interest (ROIs)
by an expert radiologist and evaluated on 106 subjects' images, which include
59 malignant and 47 benign lesions. Segmentation accuracy was evaluated by
comparing our results with ground truth masks, using the Dice similarity
coefficient (DSC), Jaccard index (JI), Hausdorff distance and precision-recall
metrics. The results indicate that the marker-controlled Watershed
transformation outperformed all other algorithms investigated.
Continuous Adaptation via Meta-Learning in Nonstationary and Competitive Environments
Authors: Maruan Al-Shedivat, Trapit Bansal, Yuri Burda, Ilya Sutskever, Igor Mordatch, Pieter Abbeel
Date: 2017-10-10 00:00:00
Proceeding: ICLR 2018 1
Tasks: Meta-Learning

Ability to continuously learn and adapt from limited experience in
nonstationary environments is an important milestone on the path towards
general intelligence. In this paper, we cast the problem of continuous
adaptation into the learning-to-learn framework. We develop a simple
gradient-based meta-learning algorithm suitable for adaptation in dynamically
changing and adversarial scenarios. Additionally, we design a new multi-agent
competitive environment, RoboSumo, and define iterated adaptation games for
testing various aspects of continuous adaptation strategies. We demonstrate
that meta-learning enables significantly more efficient adaptation than
reactive baselines in the few-shot regime. Our experiments with a population of
agents that learn and compete suggest that meta-learners are the fittest.