pwc-archive/papers-with-abstracts huggingface dataset:
Kernel Machines With Missing Responses
Authors: Tiantian Liu, Yair Goldberg
Date: 2018-06-07 00:00:00
Tasks: General Classification, regression

Missing responses is a missing data format in which outcomes are not always
observed. In this work we develop kernel machines that can handle missing
responses. First, we propose a kernel machine family that uses mainly the
complete cases. For the quadratic loss, we then propose a family of
doubly-robust kernel machines. The proposed kernel-machine estimators can be
applied to both regression and classification problems. We prove oracle
inequalities for the finite-sample differences between the kernel machine risk
and Bayes risk. We use these oracle inequalities to prove consistency and to
calculate convergence rates. We demonstrate the performance of the two proposed
kernel machine families using both a simulation study and a real-world data
analysis.
Semi-supervised and Transfer learning approaches for low resource sentiment classification
Authors: Rahul Gupta, Saurabh Sahu, Carol Espy-Wilson, Shrikanth Narayanan
Date: 2018-06-07 00:00:00
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification, Transfer Learning

Sentiment classification involves quantifying the affective reaction of a
human to a document, media item or an event. Although researchers have
investigated several methods to reliably infer sentiment from lexical, speech
and body language cues, training a model with a small set of labeled datasets
is still a challenge. For instance, in expanding sentiment analysis to new
languages and cultures, it may not always be possible to obtain comprehensive
labeled datasets. In this paper, we investigate the application of
semi-supervised and transfer learning methods to improve performances on low
resource sentiment classification tasks. We experiment with extracting dense
feature representations, pre-training and manifold regularization in enhancing
the performance of sentiment classification systems. Our goal is a coherent
implementation of these methods and we evaluate the gains achieved by these
methods in matched setting involving training and testing on a single corpus
setting as well as two cross corpora settings. In both the cases, our
experiments demonstrate that the proposed methods can significantly enhance the
model performance against a purely supervised approach, particularly in cases
involving a handful of training data.
A Large-Scale Multi-Institutional Evaluation of Advanced Discrimination Algorithms for Buried Threat Detection in Ground Penetrating Radar
Authors: Jordan M. Malof, Daniel Reichman, Andrew Karem, Hichem Frigui, Dominic K. C. Ho, Joseph N. Wilson, Wen-Hsiung Lee, William Cummings, Leslie M. Collins
Date: 2018-03-10 00:00:00
Tasks: GPR

In this paper we consider the development of algorithms for the automatic
detection of buried threats using ground penetrating radar (GPR) measurements.
GPR is one of the most studied and successful modalities for automatic buried
threat detection (BTD), and a large variety of BTD algorithms have been
proposed for it. Despite this, large-scale comparisons of GPR-based BTD
algorithms are rare in the literature. In this work we report the results of a
multi-institutional effort to develop advanced buried threat detection
algorithms for a real-world GPR BTD system. The effort involved five
institutions with substantial experience with the development of GPR-based BTD
algorithms. In this paper we report the technical details of the advanced
algorithms submitted by each institution, representing their latest technical
advances, and many state-of-the-art GPR-based BTD algorithms. We also report
the results of evaluating the algorithms from each institution on the large
experimental dataset used for development. The experimental dataset comprised
120,000 m^2 of GPR data using surface area, from 13 different lanes across two
US test sites. The data was collected using a vehicle-mounted GPR system, the
variants of which have supplied data for numerous publications. Using these
results, we identify the most successful and common processing strategies among
the submitted algorithms, and make recommendations for GPR-based BTD algorithm
design.
Learning Credible Models
Authors: Jiaxuan Wang, Jeeheh Oh, Haozhu Wang, Jenna Wiens
Date: 2017-11-08 00:00:00

In many settings, it is important that a model be capable of providing
reasons for its predictions (i.e., the model must be interpretable). However,
the model's reasoning may not conform with well-established knowledge. In such
cases, while interpretable, the model lacks \textit{credibility}. In this work,
we formally define credibility in the linear setting and focus on techniques
for learning models that are both accurate and credible. In particular, we
propose a regularization penalty, expert yielded estimates (EYE), that
incorporates expert knowledge about well-known relationships among covariates
and the outcome of interest. We give both theoretical and empirical results
comparing our proposed method to several other regularization techniques.
Across a range of settings, experiments on both synthetic and real data show
that models learned using the EYE penalty are significantly more credible than
those learned using other penalties. Applied to a large-scale patient risk
stratification task, our proposed technique results in a model whose top
features overlap significantly with known clinical risk factors, while still
achieving good predictive performance.
Scalable Natural Gradient Langevin Dynamics in Practice
Authors: Henri Palacci, Henry Hess
Date: 2018-06-07 00:00:00

Stochastic Gradient Langevin Dynamics (SGLD) is a sampling scheme for
Bayesian modeling adapted to large datasets and models. SGLD relies on the
injection of Gaussian Noise at each step of a Stochastic Gradient Descent (SGD)
update. In this scheme, every component in the noise vector is independent and
has the same scale, whereas the parameters we seek to estimate exhibit strong
variations in scale and significant correlation structures, leading to poor
convergence and mixing times. We compare different preconditioning approaches
to the normalization of the noise vector and benchmark these approaches on the
following criteria: 1) mixing times of the multivariate parameter vector, 2)
regularizing effect on small dataset where it is easy to overfit, 3) covariate
shift detection and 4) resistance to adversarial examples.
Addressing Function Approximation Error in Actor-Critic Methods
Authors: Scott Fujimoto, Herke van Hoof, David Meger
Date: 2018-02-26 00:00:00
Proceeding: ICML 2018 7
Tasks: Continuous Control, OpenAI Gym, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In value-based reinforcement learning methods such as deep Q-learning,
function approximation errors are known to lead to overestimated value
estimates and suboptimal policies. We show that this problem persists in an
actor-critic setting and propose novel mechanisms to minimize its effects on
both the actor and the critic. Our algorithm builds on Double Q-learning, by
taking the minimum value between a pair of critics to limit overestimation. We
draw the connection between target networks and overestimation bias, and
suggest delaying policy updates to reduce per-update error and further improve
performance. We evaluate our method on the suite of OpenAI gym tasks,
outperforming the state of the art in every environment tested.
Model-based active learning to detect isometric deformable objects in the wild with deep architectures
Authors: Shrinivasan Sankar, Adrien Bartoli
Date: 2018-06-07 00:00:00
Tasks: Active Learning, Object, object-detection, Object Detection, Object Recognition

In the recent past, algorithms based on Convolutional Neural Networks (CNNs)
have achieved significant milestones in object recognition. With large examples
of each object class, standard datasets train well for inter-class variability.
However, gathering sufficient data to train for a particular instance of an
object within a class is impractical. Furthermore, quantitatively assessing the
imaging conditions for each image in a given dataset is not feasible. By
generating sufficient images with known imaging conditions, we study to what
extent CNNs can cope with hard imaging conditions for instance-level
recognition in an active learning regime.
  Leveraging powerful rendering techniques to achieve instance-level detection,
we present results of training three state-of-the-art object detection
algorithms namely, Fast R-CNN, Faster R-CNN and YOLO9000, for hard imaging
conditions imposed into the scene by rendering. Our extensive experiments
produce a mean Average Precision score of 0.92 on synthetic images and 0.83 on
real images using the best performing Faster R-CNN. We show for the first time
how well detection algorithms based on deep architectures fare for each hard
imaging condition studied.
Black-box Adversarial Attacks with Limited Queries and Information
Authors: Andrew Ilyas, Logan Engstrom, Anish Athalye, Jessy Lin
Date: 2018-04-23 00:00:00
Proceeding: ICML 2018 7

Current neural network-based classifiers are susceptible to adversarial
examples even in the black-box setting, where the attacker only has query
access to the model. In practice, the threat model for real-world systems is
often more restrictive than the typical black-box model where the adversary can
observe the full output of the network on arbitrarily many chosen inputs. We
define three realistic threat models that more accurately characterize many
real-world classifiers: the query-limited setting, the partial-information
setting, and the label-only setting. We develop new attacks that fool
classifiers under these more restrictive threat models, where previous methods
would be impractical or ineffective. We demonstrate that our methods are
effective against an ImageNet classifier under our proposed threat models. We
also demonstrate a targeted black-box attack against a commercial classifier,
overcoming the challenges of limited query access, partial information, and
other practical issues to break the Google Cloud Vision API.
A Simple Method for Commonsense Reasoning
Authors: Trieu H. Trinh, Quoc V. Le
Date: 2018-06-07 00:00:00
Tasks: Common Sense Reasoning, Coreference Resolution, Diversity, Multiple-choice, Natural Language Understanding

Commonsense reasoning is a long-standing challenge for deep learning. For example, it is difficult to use neural networks to tackle the Winograd Schema dataset (Levesque et al., 2011). In this paper, we present a simple method for commonsense reasoning with neural networks, using unsupervised learning. Key to our method is the use of language models, trained on a massive amount of unlabled data, to score multiple choice questions posed by commonsense reasoning tests. On both Pronoun Disambiguation and Winograd Schema challenges, our models outperform previous state-of-the-art methods by a large margin, without using expensive annotated knowledge bases or hand-engineered features. We train an array of large RNN language models that operate at word or character level on LM-1-Billion, CommonCrawl, SQuAD, Gutenberg Books, and a customized corpus for this task and show that diversity of training data plays an important role in test performance. Further analysis also shows that our system successfully discovers important features of the context that decide the correct answer, indicating a good grasp of commonsense knowledge.
Fast Neural Machine Translation Implementation
Authors: Hieu Hoang, Tomasz Dwojak, Rihards Krislauks, Daniel Torregrosa, Kenneth Heafield
Date: 2018-05-24 00:00:00
Proceeding: WS 2018 7
Tasks: GPU, Machine Translation, Translation

This paper describes the submissions to the efficiency track for GPUs at the
Workshop for Neural Machine Translation and Generation by members of the
University of Edinburgh, Adam Mickiewicz University, Tilde and University of
Alicante. We focus on efficient implementation of the recurrent deep-learning
model as implemented in Amun, the fast inference engine for neural machine
translation. We improve the performance with an efficient mini-batching
algorithm, and by fusing the softmax operation with the k-best extraction
algorithm. Submissions using Amun were first, second and third fastest in the
GPU efficiency track.
Probabilistic Model-Agnostic Meta-Learning
Authors: Chelsea Finn, Kelvin Xu, Sergey Levine
Date: 2018-06-07 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Active Learning, Few-Shot Image Classification, Few-Shot Learning, Meta-Learning, model

Meta-learning for few-shot learning entails acquiring a prior over previous tasks and experiences, such that new tasks be learned from small amounts of data. However, a critical challenge in few-shot learning is task ambiguity: even when a powerful prior can be meta-learned from a large number of prior tasks, a small dataset for a new task can simply be too ambiguous to acquire a single model (e.g., a classifier) for that task that is accurate. In this paper, we propose a probabilistic meta-learning algorithm that can sample models for a new task from a model distribution. Our approach extends model-agnostic meta-learning, which adapts to new tasks via gradient descent, to incorporate a parameter distribution that is trained via a variational lower bound. At meta-test time, our algorithm adapts via a simple procedure that injects noise into gradient descent, and at meta-training time, the model is trained such that this stochastic adaptation procedure produces samples from the approximate model posterior. Our experimental results show that our method can sample plausible classifiers and regressors in ambiguous few-shot learning problems. We also show how reasoning about ambiguity can also be used for downstream active learning problems.
Data Summarization at Scale: A Two-Stage Submodular Approach
Authors: Marko Mitrovic, Ehsan Kazemi, Morteza Zadimoghaddam, Amin Karbasi
Date: 2018-06-07 00:00:00
Proceeding: ICML 2018 7
Tasks: Data Summarization, Vocal Bursts Valence Prediction

The sheer scale of modern datasets has resulted in a dire need for
summarization techniques that identify representative elements in a dataset.
Fortunately, the vast majority of data summarization tasks satisfy an intuitive
diminishing returns condition known as submodularity, which allows us to find
nearly-optimal solutions in linear time. We focus on a two-stage submodular
framework where the goal is to use some given training functions to reduce the
ground set so that optimizing new functions (drawn from the same distribution)
over the reduced set provides almost as much value as optimizing them over the
entire ground set. In this paper, we develop the first streaming and
distributed solutions to this problem. In addition to providing strong
theoretical guarantees, we demonstrate both the utility and efficiency of our
algorithms on real-world tasks including image summarization and ride-share
optimization.
Embedding Transfer for Low-Resource Medical Named Entity Recognition: A Case Study on Patient Mobility
Authors: Denis Newman-Griffis, Ayah Zirikly
Date: 2018-06-07 00:00:00
Proceeding: WS 2018 7
Tasks: Domain Adaptation, Medical Named Entity Recognition, named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Word Embeddings

Functioning is gaining recognition as an important indicator of global
health, but remains under-studied in medical natural language processing
research. We present the first analysis of automatically extracting
descriptions of patient mobility, using a recently-developed dataset of free
text electronic health records. We frame the task as a named entity recognition
(NER) problem, and investigate the applicability of NER techniques to mobility
extraction. As text corpora focused on patient functioning are scarce, we
explore domain adaptation of word embeddings for use in a recurrent neural
network NER system. We find that embeddings trained on a small in-domain corpus
perform nearly as well as those learned from large out-of-domain corpora, and
that domain adaptation techniques yield additional improvements in both
precision and recall. Our analysis identifies several significant challenges in
extracting descriptions of patient mobility, including the length and
complexity of annotated entities and high linguistic variability in mobility
descriptions.
Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings
Authors: John D. Co-Reyes, Yuxuan Liu, Abhishek Gupta, Benjamin Eysenbach, Pieter Abbeel, Sergey Levine
Date: 2018-06-07 00:00:00
Proceeding: ICML 2018 7
Tasks: Hierarchical Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

In this work, we take a representation learning perspective on hierarchical
reinforcement learning, where the problem of learning lower layers in a
hierarchy is transformed into the problem of learning trajectory-level
generative models. We show that we can learn continuous latent representations
of trajectories, which are effective in solving temporally extended and
multi-stage problems. Our proposed model, SeCTAR, draws inspiration from
variational autoencoders, and learns latent representations of trajectories. A
key component of this method is to learn both a latent-conditioned policy and a
latent-conditioned model which are consistent with each other. Given the same
latent, the policy generates a trajectory which should match the trajectory
predicted by the model. This model provides a built-in prediction mechanism, by
predicting the outcome of closed loop policy behavior. We propose a novel
algorithm for performing hierarchical RL with this model, combining model-based
planning in the learned latent space with an unsupervised exploration
objective. We show that our model is effective at reasoning over long horizons
with sparse rewards for several simulated tasks, outperforming standard
reinforcement learning methods and prior methods for hierarchical reasoning,
model-based planning, and exploration.
Towards Riemannian Accelerated Gradient Methods
Authors: Hongyi Zhang, Suvrit Sra
Date: 2018-06-07 00:00:00

We propose a Riemannian version of Nesterov's Accelerated Gradient algorithm
(RAGD), and show that for geodesically smooth and strongly convex problems,
within a neighborhood of the minimizer whose radius depends on the condition
number as well as the sectional curvature of the manifold, RAGD converges to
the minimizer with acceleration. Unlike the algorithm in (Liu et al., 2017)
that requires the exact solution to a nonlinear equation which in turn may be
intractable, our algorithm is constructive and computationally tractable. Our
proof exploits a new estimate sequence and a novel bound on the nonlinear
metric distortion, both ideas may be of independent interest.
Concentration of tempered posteriors and of their variational approximations
Authors: Pierre Alquier, James Ridgway
Date: 2017-06-28 00:00:00
Tasks: Collaborative Filtering, Matrix Completion

While Bayesian methods are extremely popular in statistics and machine
learning, their application to massive datasets is often challenging, when
possible at all. Indeed, the classical MCMC algorithms are prohibitively slow
when both the model dimension and the sample size are large. Variational
Bayesian methods aim at approximating the posterior by a distribution in a
tractable family. Thus, MCMC are replaced by an optimization algorithm which is
orders of magnitude faster. VB methods have been applied in such
computationally demanding applications as including collaborative filtering,
image and video processing, NLP and text processing... However, despite very
nice results in practice, the theoretical properties of these approximations
are usually not known. In this paper, we propose a general approach to prove
the concentration of variational approximations of fractional posteriors. We
apply our theory to two examples: matrix completion, and Gaussian VB.
Efficient Contextual Bandits in Non-stationary Worlds
Authors: Haipeng Luo, Chen-Yu Wei, Alekh Agarwal, John Langford
Date: 2017-08-05 00:00:00
Tasks: Multi-Armed Bandits

Most contextual bandit algorithms minimize regret against the best fixed
policy, a questionable benchmark for non-stationary environments that are
ubiquitous in applications. In this work, we develop several efficient
contextual bandit algorithms for non-stationary environments by equipping
existing methods for i.i.d. problems with sophisticated statistical tests so as
to dynamically adapt to a change in distribution.
  We analyze various standard notions of regret suited to non-stationary
environments for these algorithms, including interval regret, switching regret,
and dynamic regret. When competing with the best policy at each time, one of
our algorithms achieves regret $\mathcal{O}(\sqrt{ST})$ if there are $T$ rounds
with $S$ stationary periods, or more generally
$\mathcal{O}(\Delta^{1/3}T^{2/3})$ where $\Delta$ is some non-stationarity
measure. These results almost match the optimal guarantees achieved by an
inefficient baseline that is a variant of the classic Exp4 algorithm. The
dynamic regret result is also the first one for efficient and fully adversarial
contextual bandit.
  Furthermore, while the results above require tuning a parameter based on the
unknown quantity $S$ or $\Delta$, we also develop a parameter free algorithm
achieving regret $\min\{S^{1/4}T^{3/4}, \Delta^{1/5}T^{4/5}\}$. This improves
and generalizes the best existing result $\Delta^{0.18}T^{0.82}$ by Karnin and
Anava (2016) which only holds for the two-armed bandit problem.
Unbiased Estimation of the Value of an Optimized Policy
Authors: Elon Portugaly, Joseph J. Pfeiffer III
Date: 2018-06-07 00:00:00

Randomized trials, also known as A/B tests, are used to select between two
policies: a control and a treatment. Given a corresponding set of features, we
can ideally learn an optimized policy P that maps the A/B test data features to
action space and optimizes reward. However, although A/B testing provides an
unbiased estimator for the value of deploying B (i.e., switching from policy A
to B), direct application of those samples to learn the the optimized policy P
generally does not provide an unbiased estimator of the value of P as the
samples were observed when constructing P. In situations where the cost and
risks associated of deploying a policy are high, such an unbiased estimator is
highly desirable.
  We present a procedure for learning optimized policies and getting unbiased
estimates for the value of deploying them. We wrap any policy learning
procedure with a bagging process and obtain out-of-bag policy inclusion
decisions for each sample. We then prove that inverse-propensity-weighting
effect estimator is unbiased when applied to the optimized subset. Likewise, we
apply the same idea to obtain out-of-bag unbiased per-sample value estimate of
the measurement that is independent of the randomized treatment, and use these
estimates to build an unbiased doubly-robust effect estimator. Lastly, we
empirically shown that even when the average treatment effect is negative we
can find a positive optimized policy.