pwc-archive/papers-with-abstracts huggingface dataset:
Dual Teaching: A Practical Semi-supervised Wrapper Method
Authors: Fuqaing Liu, Chenwei Deng, Fukun Bi, Yiding Yang
Date: 2016-11-12 00:00:00

Semi-supervised wrapper methods are concerned with building effective
supervised classifiers from partially labeled data. Though previous works have
succeeded in some fields, it is still difficult to apply semi-supervised
wrapper methods to practice because the assumptions those methods rely on tend
to be unrealistic in practice. For practical use, this paper proposes a novel
semi-supervised wrapper method, Dual Teaching, whose assumptions are easy to
set up. Dual Teaching adopts two external classifiers to estimate the false
positives and false negatives of the base learner. Only if the recall of every
external classifier is greater than zero and the sum of the precision is
greater than one, Dual Teaching will train a base learner from partially
labeled data as effectively as the fully-labeled-data-trained classifier. The
effectiveness of Dual Teaching is proved in both theory and practice.
Kernel regression, minimax rates and effective dimensionality: beyond the regular case
Authors: Gilles Blanchard, Nicole Mücke
Date: 2016-11-12 00:00:00
Tasks: regression

We investigate if kernel regularization methods can achieve minimax
convergence rates over a source condition regularity assumption for the target
function. These questions have been considered in past literature, but only
under specific assumptions about the decay, typically polynomial, of the
spectrum of the the kernel mapping covariance operator. In the perspective of
distribution-free results, we investigate this issue under much weaker
assumption on the eigenvalue decay, allowing for more complex behavior that can
reflect different structure of the data at different scales.
Boost Picking: A Universal Method on Converting Supervised Classification to Semi-supervised Classification
Authors: Fuqiang Liu, Fukun Bi, Yiding Yang, Liang Chen
Date: 2016-02-18 00:00:00
Tasks: Classification, General Classification

This paper proposes a universal method, Boost Picking, to train supervised
classification models mainly by un-labeled data. Boost Picking only adopts two
weak classifiers to estimate and correct the error. It is theoretically proved
that Boost Picking could train a supervised model mainly by un-labeled data as
effectively as the same model trained by 100% labeled data, only if recalls of
the two weak classifiers are all greater than zero and the sum of precisions is
greater than one. Based on Boost Picking, we present "Test along with Training
(TawT)" to improve the generalization of supervised models. Both Boost Picking
and TawT are successfully tested in varied little data sets.
An Introduction to MM Algorithms for Machine Learning and Statistical
Authors: Hien D. Nguyen
Date: 2016-11-12 00:00:00
Tasks: BIG-bench Machine Learning

MM (majorization--minimization) algorithms are an increasingly popular tool
for solving optimization problems in machine learning and statistical
estimation. This article introduces the MM algorithm framework in general and
via three popular example applications: Gaussian mixture regressions,
multinomial logistic regressions, and support vector machines. Specific
algorithms for the three examples are derived and numerical demonstrations are
presented. Theoretical and practical aspects of MM algorithm design are
discussed.
Personalized Donor-Recipient Matching for Organ Transplantation
Authors: Jinsung Yoon, Ahmed M. Alaa, Martin Cadeiras, Mihaela van der Schaar
Date: 2016-11-12 00:00:00

Organ transplants can improve the life expectancy and quality of life for the
recipient but carries the risk of serious post-operative complications, such as
septic shock and organ rejection. The probability of a successful transplant
depends in a very subtle fashion on compatibility between the donor and the
recipient but current medical practice is short of domain knowledge regarding
the complex nature of recipient-donor compatibility. Hence a data-driven
approach for learning compatibility has the potential for significant
improvements in match quality. This paper proposes a novel system
(ConfidentMatch) that is trained using data from electronic health records.
ConfidentMatch predicts the success of an organ transplant (in terms of the 3
year survival rates) on the basis of clinical and demographic traits of the
donor and recipient. ConfidentMatch captures the heterogeneity of the donor and
recipient traits by optimally dividing the feature space into clusters and
constructing different optimal predictive models to each cluster. The system
controls the complexity of the learned predictive model in a way that allows
for assuring more granular and confident predictions for a larger number of
potential recipient-donor pairs, thereby ensuring that predictions are
"personalized" and tailored to individual characteristics to the finest
possible granularity. Experiments conducted on the UNOS heart transplant
dataset show the superiority of the prognostic value of ConfidentMatch to other
competing benchmarks; ConfidentMatch can provide predictions of success with
95% confidence for 5,489 patients of a total population of 9,620 patients,
which corresponds to 410 more patients than the most competitive benchmark
algorithm (DeepBoost).
Training IBM Watson using Automatically Generated Question-Answer Pairs
Authors: Jangho Lee, Gyuwan Kim, Jaeyoon Yoo, Changwoo Jung, Minseok Kim, Sungroh Yoon
Date: 2016-11-12 00:00:00
Tasks: Answer Generation, Question-Answer-Generation, Question Answering

IBM Watson is a cognitive computing system capable of question answering in
natural languages. It is believed that IBM Watson can understand large corpora
and answer relevant questions more effectively than any other
question-answering system currently available. To unleash the full power of
Watson, however, we need to train its instance with a large number of
well-prepared question-answer pairs. Obviously, manually generating such pairs
in a large quantity is prohibitively time consuming and significantly limits
the efficiency of Watson's training. Recently, a large-scale dataset of over 30
million question-answer pairs was reported. Under the assumption that using
such an automatically generated dataset could relieve the burden of manual
question-answer generation, we tried to use this dataset to train an instance
of Watson and checked the training efficiency and accuracy. According to our
experiments, using this auto-generated dataset was effective for training
Watson, complementing manually crafted question-answer pairs. To the best of
the authors' knowledge, this work is the first attempt to use a large-scale
dataset of automatically generated question-answer pairs for training IBM
Watson. We anticipate that the insights and lessons obtained from our
experiments will be useful for researchers who want to expedite Watson training
leveraged by automatically generated question-answer pairs.
Low Latency Anomaly Detection and Bayesian Network Prediction of Anomaly Likelihood
Authors: Derek Farren, Thai Pham, Marco Alban-Hidalgo
Date: 2016-11-11 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Time Series, Time Series Analysis

We develop a supervised machine learning model that detects anomalies in
systems in real time. Our model processes unbounded streams of data into time
series which then form the basis of a low-latency anomaly detection model.
Moreover, we extend our preliminary goal of just anomaly detection to
simultaneous anomaly prediction. We approach this very challenging problem by
developing a Bayesian Network framework that captures the information about the
parameters of the lagged regressors calibrated in the first part of our
approach and use this structure to learn local conditional probability
distributions.
Unsupervised Learning For Effective User Engagement on Social Media
Authors: Thai Pham, Camelia Simoiu
Date: 2016-11-11 00:00:00
Tasks: regression

In this paper, we investigate the effectiveness of unsupervised feature
learning techniques in predicting user engagement on social media.
Specifically, we compare two methods to predict the number of feedbacks (i.e.,
comments) that a blog post is likely to receive. We compare Principal Component
Analysis (PCA) and sparse Autoencoder to a baseline method where the data are
only centered and scaled, on each of two models: Linear Regression and
Regression Tree. We find that unsupervised learning techniques significantly
improve the prediction accuracy on both models. For the Linear Regression
model, sparse Autoencoder achieves the best result, with an improvement in the
root mean squared error (RMSE) on the test set of 42% over the baseline method.
For the Regression Tree model, PCA achieves the best result, with an
improvement in RMSE of 15% over the baseline.
Annealing Gaussian into ReLU: a New Sampling Strategy for Leaky-ReLU RBM
Authors: Chun-Liang Li, Siamak Ravanbakhsh, Barnabas Poczos
Date: 2016-11-11 00:00:00

Restricted Boltzmann Machine (RBM) is a bipartite graphical model that is
used as the building block in energy-based deep generative models. Due to
numerical stability and quantifiability of the likelihood, RBM is commonly used
with Bernoulli units. Here, we consider an alternative member of exponential
family RBM with leaky rectified linear units -- called leaky RBM. We first
study the joint and marginal distributions of leaky RBM under different
leakiness, which provides us important insights by connecting the leaky RBM
model and truncated Gaussian distributions. The connection leads us to a simple
yet efficient method for sampling from this model, where the basic idea is to
anneal the leakiness rather than the energy; -- i.e., start from a fully
Gaussian/Linear unit and gradually decrease the leakiness over iterations. This
serves as an alternative to the annealing of the temperature parameter and
enables numerical estimation of the likelihood that are more efficient and more
accurate than the commonly used annealed importance sampling (AIS). We further
demonstrate that the proposed sampling algorithm enjoys faster mixing property
than contrastive divergence algorithm, which benefits the training without any
additional computational cost.
Effective sparse representation of X-Ray medical images
Authors: Laura Rebollo-Neira
Date: 2016-11-11 00:00:00

Effective sparse representation of X-Ray medical images within the context of
data reduction is considered. The proposed framework is shown to render an
enormous reduction in the cardinality of the data set required to represent
this class of images at very good quality. The particularity of the approach is
that it can be implemented at very competitive processing time and low memory
requirements
Recovery Guarantee of Non-negative Matrix Factorization via Alternating Updates
Authors: Yuanzhi Li, YIngyu Liang, Andrej Risteski
Date: 2016-11-11 00:00:00
Proceeding: NeurIPS 2016 12

Non-negative matrix factorization is a popular tool for decomposing data into
feature and weight matrices under non-negativity constraints. It enjoys
practical success but is poorly understood theoretically. This paper proposes
an algorithm that alternates between decoding the weights and updating the
features, and shows that assuming a generative model of the data, it provably
recovers the ground-truth under fairly mild conditions. In particular, its only
essential requirement on features is linear independence. Furthermore, the
algorithm uses ReLU to exploit the non-negativity for decoding the weights, and
thus can tolerate adversarial noise that can potentially be as large as the
signal, and can tolerate unbiased noise much larger than the signal. The
analysis relies on a carefully designed coupling between two potential
functions, which we believe is of independent interest.
Towards the Science of Security and Privacy in Machine Learning
Authors: Nicolas Papernot, Patrick McDaniel, Arunesh Sinha, Michael Wellman
Date: 2016-11-11 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Advances in machine learning (ML) in recent years have enabled a dizzying
array of applications such as data analytics, autonomous systems, and security
diagnostics. ML is now pervasive---new systems and models are being deployed in
every domain imaginable, leading to rapid and widespread deployment of software
based inference and decision making. There is growing recognition that ML
exposes new vulnerabilities in software systems, yet the technical community's
understanding of the nature and extent of these vulnerabilities remains
limited. We systematize recent findings on ML security and privacy, focusing on
attacks identified on these systems and defenses crafted to date. We articulate
a comprehensive threat model for ML, and categorize attacks and defenses within
an adversarial framework. Key insights resulting from works both in the ML and
security communities are identified and the effectiveness of approaches are
related to structural elements of ML algorithms and the data used to train
them. We conclude by formally exploring the opposing relationship between model
accuracy and resilience to adversarial manipulation. Through these
explorations, we show that there are (possibly unavoidable) tensions between
model complexity, accuracy, and resilience that must be calibrated for the
environments in which they will be used.
HoneyFaces: Increasing the Security and Privacy of Authentication Using Synthetic Facial Images
Authors: Mor Ohana, Orr Dunkelman, Stuart Gibson, Margarita Osadchy
Date: 2016-11-11 00:00:00

One of the main challenges faced by Biometric-based authentication systems is
the need to offer secure authentication while maintaining the privacy of the
biometric data. Previous solutions, such as Secure Sketch and Fuzzy Extractors,
rely on assumptions that cannot be guaranteed in practice, and often affect the
authentication accuracy.
  In this paper, we introduce HoneyFaces: the concept of adding a large set of
synthetic faces (indistinguishable from real) into the biometric "password
file". This password inflation protects the privacy of users and increases the
security of the system without affecting the accuracy of the authentication. In
particular, privacy for the real users is provided by "hiding" them among a
large number of fake users (as the distributions of synthetic and real faces
are equal). In addition to maintaining the authentication accuracy, and thus
not affecting the security of the authentication process, HoneyFaces offer
several security improvements: increased exfiltration hardness, improved
leakage detection, and the ability to use a Two-server setting like in
HoneyWords. Finally, HoneyFaces can be combined with other security and privacy
mechanisms for biometric data.
  We implemented the HoneyFaces system and tested it with a password file
composed of 270 real users. The "password file" was then inflated to
accommodate up to $2^{36.5}$ users (resulting in a 56.6 TB "password file"). At
the same time, the inclusion of additional faces does not affect the true
acceptance rate or false acceptance rate which were 93.33\% and 0.01\%,
respectively.
Plug-and-Play ADMM for Image Restoration: Fixed Point Convergence and Applications
Authors: Stanley H. Chan, Xiran Wang, Omar A. Elgendy
Date: 2016-05-05 00:00:00
Tasks: Denoising, Image Denoising, Image Restoration, Super-Resolution

Alternating direction method of multiplier (ADMM) is a widely used algorithm
for solving constrained optimization problems in image restoration. Among many
useful features, one critical feature of the ADMM algorithm is its modular
structure which allows one to plug in any off-the-shelf image denoising
algorithm for a subproblem in the ADMM algorithm. Because of the plug-in
nature, this type of ADMM algorithms is coined the name "Plug-and-Play ADMM".
Plug-and-Play ADMM has demonstrated promising empirical results in a number of
recent papers. However, it is unclear under what conditions and by using what
denoising algorithms would it guarantee convergence. Also, since Plug-and-Play
ADMM uses a specific way to split the variables, it is unclear if fast
implementation can be made for common Gaussian and Poissonian image restoration
problems.
  In this paper, we propose a Plug-and-Play ADMM algorithm with provable fixed
point convergence. We show that for any denoising algorithm satisfying an
asymptotic criteria, called bounded denoisers, Plug-and-Play ADMM converges to
a fixed point under a continuation scheme. We also present fast implementations
for two image restoration problems on super-resolution and single-photon
imaging. We compare Plug-and-Play ADMM with state-of-the-art algorithms in each
problem type, and demonstrate promising experimental results of the algorithm.
Applying Chatbots to the Internet of Things: Opportunities and Architectural Elements
Authors: Rohan Kar, Rishin Haldar
Date: 2016-11-11 00:00:00

Internet of Things (IoT) is emerging as a significant technology in shaping
the future by connecting physical devices or things with internet. It also
presents various opportunities for intersection of other technological trends
which can allow it to become even more intelligent and efficient. In this paper
we focus our attention on the integration of Intelligent Conversational
Software Agents or Chatbots with IoT. Literature surveys have looked into
various applications, features, underlying technologies and known challenges of
IoT. On the other hand, Chatbots are being adopted in greater numbers due to
major strides in development of platforms and frameworks. The novelty of this
paper lies in the specific integration of Chatbots in the IoT scenario. We
analyzed the shortcomings of existing IoT systems and put forward ways to
tackle them by incorporating chatbots. A general architecture is proposed for
implementing such a system, as well as platforms and frameworks, both
commercial and open source, which allow for implementation of such systems.
Identification of the newer challenges and possible future directions with this
new integration, have also been addressed.
Understanding the 2016 US Presidential Election using ecological inference and distribution regression with census microdata
Authors: Seth Flaxman, Danica J. Sutherland, Yu-Xiang Wang, Yee Whye Teh
Date: 2016-11-11 00:00:00
Tasks: regression

We combine fine-grained spatially referenced census data with the vote outcomes from the 2016 US presidential election. Using this dataset, we perform ecological inference using distribution regression (Flaxman et al, KDD 2015) with a multinomial-logit regression so as to model the vote outcome Trump, Clinton, Other / Didn't vote as a function of demographic and socioeconomic features. Ecological inference allows us to estimate "exit poll" style results like what was Trump's support among white women, but for entirely novel categories. We also perform exploratory data analysis to understand which census variables are predictive of voting for Trump, voting for Clinton, or not voting for either. All of our methods are implemented in Python and R, and are available online for replication.
Fast L1-NMF for Multiple Parametric Model Estimation
Authors: Mariano Tepper, Guillermo Sapiro
Date: 2016-10-18 00:00:00
Tasks: model, parameter estimation

In this work we introduce a comprehensive algorithmic pipeline for multiple
parametric model estimation. The proposed approach analyzes the information
produced by a random sampling algorithm (e.g., RANSAC) from a machine
learning/optimization perspective, using a \textit{parameterless} biclustering
algorithm based on L1 nonnegative matrix factorization (L1-NMF). The proposed
framework exploits consistent patterns that naturally arise during the RANSAC
execution, while explicitly avoiding spurious inconsistencies. Contrarily to
the main trends in the literature, the proposed technique does not impose
non-intersecting parametric models. A new accelerated algorithm to compute
L1-NMFs allows to handle medium-sized problems faster while also extending the
usability of the algorithm to much larger datasets. This accelerated algorithm
has applications in any other context where an L1-NMF is needed, beyond the
biclustering approach to parameter estimation here addressed. We accompany the
algorithmic presentation with theoretical foundations and numerous and diverse
examples.
MCMC Shape Sampling for Image Segmentation with Nonparametric Shape Priors
Authors: Ertunc Erdil, Sinan Yildirim, Müjdat Çetin, Tolga Taşdizen
Date: 2016-11-11 00:00:00
Proceeding: CVPR 2016 6
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Segmenting images of low quality or with missing data is a challenging
problem. Integrating statistical prior information about the shapes to be
segmented can improve the segmentation results significantly. Most shape-based
segmentation algorithms optimize an energy functional and find a point estimate
for the object to be segmented. This does not provide a measure of the degree
of confidence in that result, neither does it provide a picture of other
probable solutions based on the data and the priors. With a statistical view,
addressing these issues would involve the problem of characterizing the
posterior densities of the shapes of the objects to be segmented. For such
characterization, we propose a Markov chain Monte Carlo (MCMC) sampling-based
image segmentation algorithm that uses statistical shape priors. In addition to
better characterization of the statistical structure of the problem, such an
approach would also have the potential to address issues with getting stuck at
local optima, suffered by existing shape-based segmentation methods. Our
approach is able to characterize the posterior probability density in the space
of shapes through its samples, and to return multiple solutions, potentially
from different modes of a multimodal probability density, which would be
encountered, e.g., in segmenting objects from multiple shape classes. We
present promising results on a variety of data sets. We also provide an
extension for segmenting shapes of objects with parts that can go through
independent shape variations. This extension involves the use of local shape
priors on object parts and provides robustness to limitations in shape training
data size.
A Comparative Analysis of classification data mining techniques : Deriving key factors useful for predicting students performance
Authors: Muhammed Salman Shamsi, Jhansi Lakshmi
Date: 2016-06-18 00:00:00
Tasks: General Classification, Prediction

Students opting for Engineering as their discipline is increasing rapidly.
But due to various factors and inappropriate primary education in India,
failure rates are high. Students are unable to excel in core engineering
because of complex and mathematical subjects. Hence, they fail in such
subjects. With the help of data mining techniques, we can predict the
performance of students in terms of grades and failure in subjects. This paper
performs a comparative analysis of various classification techniques, such as
Na\"ive Bayes, LibSVM, J48, Random Forest, and JRip and tries to choose best
among these. Based on the results obtained, we found that Na\"ive Bayes is the
most accurate method in terms of students failure prediction and JRip is most
accurate in terms of students grade prediction. We also found that JRip
marginally differs from Na\"ive Bayes in terms of accuracy for students failure
prediction and gives us a set of rules from which we derive the key factors
influencing students performance. Finally, we suggest various ways to mitigate
these factors. This study is limited to Indian Education system scenarios.
However, the factors found can be helpful in other scenarios as well.