pwc-archive/papers-with-abstracts huggingface dataset:
Feature Importance Measure for Non-linear Learning Algorithms
Authors: Marina M. -C. Vidovic, Nico Görnitz, Klaus-Robert Müller, Marius Kloft
Date: 2016-11-22 00:00:00
Tasks: Feature Importance, Prediction

Complex problems may require sophisticated, non-linear learning methods such
as kernel machines or deep neural networks to achieve state of the art
prediction accuracies. However, high prediction accuracies are not the only
objective to consider when solving problems using machine learning. Instead,
particular scientific applications require some explanation of the learned
prediction function. Unfortunately, most methods do not come with out of the
box straight forward interpretation. Even linear prediction functions are not
straight forward to explain if features exhibit complex correlation structure.
  In this paper, we propose the Measure of Feature Importance (MFI). MFI is
general and can be applied to any arbitrary learning machine (including kernel
machines and deep learning). MFI is intrinsically non-linear and can detect
features that by itself are inconspicuous and only impact the prediction
function through their interaction with other features. Lastly, MFI can be used
for both --- model-based feature importance and instance-based feature
importance (i.e, measuring the importance of a feature for a particular data
point).
Sar image despeckling based on nonlocal similarity sparse decomposition
Authors: Cheng-Wei Sang, Hong Sun, Quisong Xia
Date: 2016-11-22 00:00:00
Tasks: Sar Image Despeckling

This letter presents a method of synthetic aperture radar (SAR) image
despeckling aimed to preserve the detail information while suppressing speckle
noise. This method combines the nonlocal self-similarity partition and a
proposed modified sparse decomposition. The nonlocal partition method groups a
series of structure-similarity data sets. Each data set has a good sparsity for
learning an over-complete dictionary in sparse representation. In the sparse
decomposition, we propose a novel method to identify principal atoms from
over-complete dictionary to form a principal dictionary. Despeckling is
performed on each data set over the principal dictionary with principal atoms.
Experimental results demonstrate that the proposed method can achieve high
performances in terms of both speckle noise reduction and structure details
preservation.
Randomized Distributed Mean Estimation: Accuracy vs Communication
Authors: Jakub Konečný, Peter Richtárik
Date: 2016-11-22 00:00:00

We consider the problem of estimating the arithmetic average of a finite
collection of real vectors stored in a distributed fashion across several
compute nodes subject to a communication budget constraint. Our analysis does
not rely on any statistical assumptions about the source of the vectors. This
problem arises as a subproblem in many applications, including reduce-all
operations within algorithms for distributed and federated optimization and
learning. We propose a flexible family of randomized algorithms exploring the
trade-off between expected communication cost and estimation error. Our family
contains the full-communication and zero-error method on one extreme, and an
$\epsilon$-bit communication and ${\cal O}\left(1/(\epsilon n)\right)$ error
method on the opposite extreme. In the special case where we communicate, in
expectation, a single bit per coordinate of each vector, we improve upon
existing results by obtaining $\mathcal{O}(r/n)$ error, where $r$ is the number
of bits used to represent a floating point value.
Gendered Conversation in a Social Game-Streaming Platform
Authors: Supun Nakandala, Giovanni Luca Ciampaglia, Norman Makoto Su, Yong-Yeol Ahn
Date: 2016-11-20 00:00:00

Online social media and games are increasingly replacing offline social
activities. Social media is now an indispensable mode of communication; online
gaming is not only a genuine social activity but also a popular spectator
sport. With support for anonymity and larger audiences, online interaction
shrinks social and geographical barriers. Despite such benefits, social
disparities such as gender inequality persist in online social media. In
particular, online gaming communities have been criticized for persistent
gender disparities and objectification. As gaming evolves into a social
platform, persistence of gender disparity is a pressing question. Yet, there
are few large-scale, systematic studies of gender inequality and
objectification in social gaming platforms. Here we analyze more than one
billion chat messages from Twitch, a social game-streaming platform, to study
how the gender of streamers is associated with the nature of conversation.
Using a combination of computational text analysis methods, we show that
gendered conversation and objectification is prevalent in chats. Female
streamers receive significantly more objectifying comments while male streamers
receive more game-related comments. This difference is more pronounced for
popular streamers. There also exists a large number of users who post only on
female or male streams. Employing a neural vector-space embedding (paragraph
vector) method, we analyze gendered chat messages and create prediction models
that (i) identify the gender of streamers based on messages posted in the
channel and (ii) identify the gender a viewer prefers to watch based on their
chat messages. Our findings suggest that disparities in social game-streaming
platforms is a nuanced phenomenon that involves the gender of streamers as well
as those who produce gendered and game-related conversation.
Self-learning Scene-specific Pedestrian Detectors using a Progressive Latent Model
Authors: Qixiang Ye, Tianliang Zhang, Qiang Qiu, Baochang Zhang, Jie Chen, Guillermo Sapiro
Date: 2016-11-22 00:00:00
Proceeding: CVPR 2017 7
Tasks: Object, Object Discovery, Object Localization, Pedestrian Detection, Self-Learning, Transfer Learning, Weakly-supervised Learning

In this paper, a self-learning approach is proposed towards solving
scene-specific pedestrian detection problem without any human' annotation
involved. The self-learning approach is deployed as progressive steps of object
discovery, object enforcement, and label propagation. In the learning
procedure, object locations in each frame are treated as latent variables that
are solved with a progressive latent model (PLM). Compared with conventional
latent models, the proposed PLM incorporates a spatial regularization term to
reduce ambiguities in object proposals and to enforce object localization, and
also a graph-based label propagation to discover harder instances in adjacent
frames. With the difference of convex (DC) objective functions, PLM can be
efficiently optimized with a concave-convex programming and thus guaranteeing
the stability of self-learning. Extensive experiments demonstrate that even
without annotation the proposed self-learning approach outperforms weakly
supervised learning approaches, while achieving comparable performance with
transfer learning and fully supervised approaches.
Revisiting Visual Question Answering Baselines
Authors: Allan Jabri, Armand Joulin, Laurens van der Maaten
Date: 2016-06-27 00:00:00
Tasks: Binary Classification, Multiple-choice, Triplet, Visual Grounding, Visual Question Answering, Visual Question Answering (VQA)

Visual question answering (VQA) is an interesting learning setting for
evaluating the abilities and shortcomings of current systems for image
understanding. Many of the recently proposed VQA systems include attention or
memory mechanisms designed to support "reasoning". For multiple-choice VQA,
nearly all of these systems train a multi-class classifier on image and
question features to predict an answer. This paper questions the value of these
common practices and develops a simple alternative model based on binary
classification. Instead of treating answers as competing choices, our model
receives the answer as input and predicts whether or not an
image-question-answer triplet is correct. We evaluate our model on the Visual7W
Telling and the VQA Real Multiple Choice tasks, and find that even simple
versions of our model perform competitively. Our best model achieves
state-of-the-art performance on the Visual7W Telling task and compares
surprisingly well with the most complex systems proposed for the VQA Real
Multiple Choice task. We explore variants of the model and study its
transferability between both datasets. We also present an error analysis of our
model that suggests a key problem of current VQA systems lies in the lack of
visual grounding of concepts that occur in the questions and answers. Overall,
our results suggest that the performance of current VQA systems is not
significantly better than that of systems designed to exploit dataset biases.
A causal framework for discovering and removing direct and indirect discrimination
Authors: Lu Zhang, Yongkai Wu, Xintao Wu
Date: 2016-11-22 00:00:00
Tasks: Decision Making

Anti-discrimination is an increasingly important task in data science. In
this paper, we investigate the problem of discovering both direct and indirect
discrimination from the historical data, and removing the discriminatory
effects before the data is used for predictive analysis (e.g., building
classifiers). We make use of the causal network to capture the causal structure
of the data. Then we model direct and indirect discrimination as the
path-specific effects, which explicitly distinguish the two types of
discrimination as the causal effects transmitted along different paths in the
network. Based on that, we propose an effective algorithm for discovering
direct and indirect discrimination, as well as an algorithm for precisely
removing both types of discrimination while retaining good data utility.
Different from previous works, our approaches can ensure that the predictive
models built from the modified data will not incur discrimination in decision
making. Experiments using real datasets show the effectiveness of our
approaches.
Variational Intrinsic Control
Authors: Karol Gregor, Danilo Jimenez Rezende, Daan Wierstra
Date: 2016-11-22 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL), Unsupervised Reinforcement Learning

In this paper we introduce a new unsupervised reinforcement learning method
for discovering the set of intrinsic options available to an agent. This set is
learned by maximizing the number of different states an agent can reliably
reach, as measured by the mutual information between the set of options and
option termination states. To this end, we instantiate two policy gradient
based algorithms, one that creates an explicit embedding space of options and
one that represents options implicitly. The algorithms also provide an explicit
measure of empowerment in a given state that can be used by an empowerment
maximizing agent. The algorithm scales well with function approximation and we
demonstrate the applicability of the algorithm on a range of tasks.
A Randomized Rounding Algorithm for Sparse PCA
Authors: Kimon Fountoulakis, Abhisek Kundu, Eugenia-Maria Kontopoulou, Petros Drineas
Date: 2015-08-13 00:00:00

We present and analyze a simple, two-step algorithm to approximate the
optimal solution of the sparse PCA problem. Our approach first solves a L1
penalized version of the NP-hard sparse PCA optimization problem and then uses
a randomized rounding strategy to sparsify the resulting dense solution. Our
main theoretical result guarantees an additive error approximation and provides
a tradeoff between sparsity and accuracy. Our experimental evaluation indicates
that our approach is competitive in practice, even compared to state-of-the-art
toolboxes such as Spasm.
Inducing Interpretable Representations with Variational Autoencoders
Authors: N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem van de Meent, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr
Date: 2016-11-22 00:00:00
Tasks: General Classification, Variational Inference

We develop a framework for incorporating structured graphical models in the
\emph{encoders} of variational autoencoders (VAEs) that allows us to induce
interpretable representations through approximate variational inference. This
allows us to both perform reasoning (e.g. classification) under the structural
constraints of a given graphical model, and use deep generative models to deal
with messy, high-dimensional domains where it is often difficult to model all
the variation. Learning in this framework is carried out end-to-end with a
variational objective, applying to both unsupervised and semi-supervised
schemes.
Can Co-robots Learn to Teach?
Authors: Harshal Maske, Emily Kieson, Girish Chowdhary, Charles Abramson
Date: 2016-11-22 00:00:00
Tasks: Clustering, Reinforcement Learning

We explore beyond existing work on learning from demonstration by asking the
question: Can robots learn to teach?, that is, can a robot autonomously learn
an instructional policy from expert demonstration and use it to instruct or
collaborate with humans in executing complex tasks in uncertain environments?
In this paper we pursue a solution to this problem by leveraging the idea that
humans often implicitly decompose a higher level task into several subgoals
whose execution brings the task closer to completion. We propose Dirichlet
process based non-parametric Inverse Reinforcement Learning (DPMIRL) approach
for reward based unsupervised clustering of task space into subgoals. This
approach is shown to capture the latent subgoals that a human teacher would
have utilized to train a novice. The notion of action primitive is introduced
as the means to communicate instruction policy to humans in the least
complicated manner, and as a computationally efficient tool to segment
demonstration data. We evaluate our approach through experiments on hydraulic
actuated scaled model of an excavator and evaluate and compare different
teaching strategies utilized by the robot.
Poisson Random Fields for Dynamic Feature Models
Authors: Valerio Perrone, Paul A. Jenkins, Dario Spano, Yee Whye Teh
Date: 2016-11-22 00:00:00

We present the Wright-Fisher Indian buffet process (WF-IBP), a probabilistic
model for time-dependent data assumed to have been generated by an unknown
number of latent features. This model is suitable as a prior in Bayesian
nonparametric feature allocation models in which the features underlying the
observed data exhibit a dependency structure over time. More specifically, we
establish a new framework for generating dependent Indian buffet processes,
where the Poisson random field model from population genetics is used as a way
of constructing dependent beta processes. Inference in the model is complex,
and we describe a sophisticated Markov Chain Monte Carlo algorithm for exact
posterior simulation. We apply our construction to develop a nonparametric
focused topic model for collections of time-stamped text documents and test it
on the full corpus of NIPS papers published from 1987 to 2015.
Clustering with Same-Cluster Queries
Authors: Hassan Ashtiani, Shrinu Kushagra, Shai Ben-David
Date: 2016-06-08 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Clustering

We propose a framework for Semi-Supervised Active Clustering framework
(SSAC), where the learner is allowed to interact with a domain expert, asking
whether two given instances belong to the same cluster or not. We study the
query and computational complexity of clustering in this framework. We consider
a setting where the expert conforms to a center-based clustering with a notion
of margin. We show that there is a trade off between computational complexity
and query complexity; We prove that for the case of $k$-means clustering (i.e.,
when the expert conforms to a solution of $k$-means), having access to
relatively few such queries allows efficient solutions to otherwise NP hard
problems.
  In particular, we provide a probabilistic polynomial-time (BPP) algorithm for
clustering in this setting that asks $O\big(k^2\log k + k\log n)$ same-cluster
queries and runs with time complexity $O\big(kn\log n)$ (where $k$ is the
number of clusters and $n$ is the number of instances). The algorithm succeeds
with high probability for data satisfying margin conditions under which,
without queries, we show that the problem is NP hard. We also prove a lower
bound on the number of queries needed to have a computationally efficient
clustering algorithm in this setting.
Mapping chemical performance on molecular structures using locally interpretable explanations
Authors: Leanne S. Whitmore, Anthe George, Corey M. Hudson
Date: 2016-11-22 00:00:00

In this work, we present an application of Locally Interpretable
Machine-Agnostic Explanations to 2-D chemical structures. Using this framework
we are able to provide a structural interpretation for an existing black-box
model for classifying biologically produced fuel compounds with regard to
Research Octane Number. This method of "painting" locally interpretable
explanations onto 2-D chemical structures replicates the chemical intuition of
synthetic chemists, allowing researchers in the field to directly accept,
reject, inform and evaluate decisions underlying inscrutably complex
quantitative structure-activity relationship models.
Achieving non-discrimination in data release
Authors: Lu Zhang, Yongkai Wu, Xintao Wu
Date: 2016-11-22 00:00:00
Tasks: Attribute

Discrimination discovery and prevention/removal are increasingly important
tasks in data mining. Discrimination discovery aims to unveil discriminatory
practices on the protected attribute (e.g., gender) by analyzing the dataset of
historical decision records, and discrimination prevention aims to remove
discrimination by modifying the biased data before conducting predictive
analysis. In this paper, we show that the key to discrimination discovery and
prevention is to find the meaningful partitions that can be used to provide
quantitative evidences for the judgment of discrimination. With the support of
the causal graph, we present a graphical condition for identifying a meaningful
partition. Based on that, we develop a simple criterion for the claim of
non-discrimination, and propose discrimination removal algorithms which
accurately remove discrimination while retaining good data utility. Experiments
using real datasets show the effectiveness of our approaches.
TreeView: Peeking into Deep Neural Networks Via Feature-Space Partitioning
Authors: Jayaraman J. Thiagarajan, Bhavya Kailkhura, Prasanna Sattigeri, Karthikeyan Natesan Ramamurthy
Date: 2016-11-22 00:00:00

With the advent of highly predictive but opaque deep learning models, it has
become more important than ever to understand and explain the predictions of
such models. Existing approaches define interpretability as the inverse of
complexity and achieve interpretability at the cost of accuracy. This
introduces a risk of producing interpretable but misleading explanations. As
humans, we are prone to engage in this kind of behavior \cite{mythos}. In this
paper, we take a step in the direction of tackling the problem of
interpretability without compromising the model accuracy. We propose to build a
Treeview representation of the complex model via hierarchical partitioning of
the feature space, which reveals the iterative rejection of unlikely class
labels until the correct association is predicted.
3D Image Reconstruction from X-Ray Measurements with Overlap
Authors: Maria Klodt, Raphael Hauser
Date: 2016-11-22 00:00:00
Tasks: Image Reconstruction, Vocal Bursts Type Prediction

3D image reconstruction from a set of X-ray projections is an important image
reconstruction problem, with applications in medical imaging, industrial
inspection and airport security. The innovation of X-ray emitter arrays allows
for a novel type of X-ray scanners with multiple simultaneously emitting
sources. However, two or more sources emitting at the same time can yield
measurements from overlapping rays, imposing a new type of image reconstruction
problem based on nonlinear constraints. Using traditional linear reconstruction
methods, respective scanner geometries have to be implemented such that no rays
overlap, which severely restricts the scanner design. We derive a new type of
3D image reconstruction model with nonlinear constraints, based on measurements
with overlapping X-rays. Further, we show that the arising optimization problem
is partially convex, and present an algorithm to solve it. Experiments show
highly improved image reconstruction results from both simulated and real-world
measurements.
Smart Library: Identifying Books in a Library using Richly Supervised Deep Scene Text Reading
Authors: Xiao Yang, Dafang He, Wenyi Huang, Zihan Zhou, Alex Ororbia, Dan Kifer, C. Lee Giles
Date: 2016-11-22 00:00:00
Tasks: Management, Retrieval, Scene Text Detection, Scene Text Recognition, Text Detection

Physical library collections are valuable and long standing resources for
knowledge and learning. However, managing books in a large bookshelf and
finding books on it often leads to tedious manual work, especially for large
book collections where books might be missing or misplaced. Recently, deep
neural models, such as Convolutional Neural Networks (CNN) and Recurrent Neural
Networks (RNN) have achieved great success for scene text detection and
recognition. Motivated by these recent successes, we aim to investigate their
viability in facilitating book management, a task that introduces further
challenges including large amounts of cluttered scene text, distortion, and
varied lighting conditions. In this paper, we present a library inventory
building and retrieval system based on scene text reading methods. We
specifically design our scene text recognition model using rich supervision to
accelerate training and achieve state-of-the-art performance on several
benchmark datasets. Our proposed system has the potential to greatly reduce the
amount of human labor required in managing book inventories as well as the
space needed to store book information.
Randomized Mechanisms for Selling Reserved Instances in Cloud
Authors: Jia Zhang, Weidong Ma, Tao Qin, Xiaoming Sun, Tie-Yan Liu
Date: 2016-11-22 00:00:00
Tasks: Cloud Computing

Selling reserved instances (or virtual machines) is a basic service in cloud
computing. In this paper, we consider a more flexible pricing model for
instance reservation, in which a customer can propose the time length and
number of resources of her request, while in today's industry, customers can
only choose from several predefined reservation packages. Under this model, we
design randomized mechanisms for customers coming online to optimize social
welfare and providers' revenue.
  We first consider a simple case, where the requests from the customers do not
vary too much in terms of both length and value density. We design a randomized
mechanism that achieves a competitive ratio $\frac{1}{42}$ for both
\emph{social welfare} and \emph{revenue}, which is a improvement as there is
usually no revenue guarantee in previous works such as
\cite{azar2015ec,wang2015selling}. This ratio can be improved up to
$\frac{1}{11}$ when we impose a realistic constraint on the maximum number of
resources used by each request. On the hardness side, we show an upper bound
$\frac{1}{3}$ on competitive ratio for any randomized mechanism. We then extend
our mechanism to the general case and achieve a competitive ratio
$\frac{1}{42\log k\log T}$ for both social welfare and revenue, where $T$ is
the ratio of the maximum request length to the minimum request length and $k$
is the ratio of the maximum request value density to the minimum request value
density. This result outperforms the previous upper bound $\frac{1}{CkT}$ for
deterministic mechanisms \cite{wang2015selling}. We also prove an upper bound
$\frac{2}{\log 8kT}$ for any randomized mechanism. All the mechanisms we
provide are in a greedy style. They are truthful and easy to be integrated into
practical cloud systems.