pwc-archive/papers-with-abstracts huggingface dataset:
Tensor Decompositions for Identifying Directed Graph Topologies and Tracking Dynamic Networks
Authors: Yanning Shen, Brian Baingana, Georgios B. Giannakis
Date: 2016-10-26 00:00:00
Tasks: Tensor Decomposition

Directed networks are pervasive both in nature and engineered systems, often
underlying the complex behavior observed in biological systems, microblogs and
social interactions over the web, as well as global financial markets. Since
their structures are often unobservable, in order to facilitate network
analytics, one generally resorts to approaches capitalizing on measurable nodal
processes to infer the unknown topology. Structural equation models (SEMs) are
capable of incorporating exogenous inputs to resolve inherent directional
ambiguities. However, conventional SEMs assume full knowledge of exogenous
inputs, which may not be readily available in some practical settings. The
present paper advocates a novel SEM-based topology inference approach that
entails factorization of a three-way tensor, constructed from the observed
nodal data, using the well-known parallel factor (PARAFAC) decomposition. It
turns out that second-order piecewise stationary statistics of exogenous
variables suffice to identify the hidden topology. Capitalizing on the
uniqueness properties inherent to high-order tensor factorizations, it is shown
that topology identification is possible under reasonably mild conditions. In
addition, to facilitate real-time operation and inference of time-varying
networks, an adaptive (PARAFAC) tensor decomposition scheme which tracks the
topology-revealing tensor factors is developed. Extensive tests on simulated
and real stock quote data demonstrate the merits of the novel tensor-based
approach.
Predicting Counterfactuals from Large Historical Data and Small Randomized Trials
Authors: Nir Rosenfeld, Yishay Mansour, Elad Yom-Tov
Date: 2016-10-24 00:00:00
Tasks: counterfactual

When a new treatment is considered for use, whether a pharmaceutical drug or
a search engine ranking algorithm, a typical question that arises is, will its
performance exceed that of the current treatment? The conventional way to
answer this counterfactual question is to estimate the effect of the new
treatment in comparison to that of the conventional treatment by running a
controlled, randomized experiment. While this approach theoretically ensures an
unbiased estimator, it suffers from several drawbacks, including the difficulty
in finding representative experimental populations as well as the cost of
running such trials. Moreover, such trials neglect the huge quantities of
available control-condition data which are often completely ignored.
  In this paper we propose a discriminative framework for estimating the
performance of a new treatment given a large dataset of the control condition
and data from a small (and possibly unrepresentative) randomized trial
comparing new and old treatments. Our objective, which requires minimal
assumptions on the treatments, models the relation between the outcomes of the
different conditions. This allows us to not only estimate mean effects but also
to generate individual predictions for examples outside the randomized sample.
  We demonstrate the utility of our approach through experiments in three
areas: Search engine operation, treatments to diabetes patients, and market
value estimation for houses. Our results demonstrate that our approach can
reduce the number and size of the currently performed randomized controlled
experiments, thus saving significant time, money and effort on the part of
practitioners.
Automatic measurement of vowel duration via structured prediction
Authors: Yossi Adi, Joseph Keshet, Emily Cibelli, Erin Gustafson, Cynthia Clopper, Matthew Goldrick
Date: 2016-10-26 00:00:00
Tasks: Prediction, Structured Prediction

A key barrier to making phonetic studies scalable and replicable is the need
to rely on subjective, manual annotation. To help meet this challenge, a
machine learning algorithm was developed for automatic measurement of a widely
used phonetic measure: vowel duration. Manually-annotated data were used to
train a model that takes as input an arbitrary length segment of the acoustic
signal containing a single vowel that is preceded and followed by consonants
and outputs the duration of the vowel. The model is based on the structured
prediction framework. The input signal and a hypothesized set of a vowel's
onset and offset are mapped to an abstract vector space by a set of acoustic
feature functions. The learning algorithm is trained in this space to minimize
the difference in expectations between predicted and manually-measured vowel
durations. The trained model can then automatically estimate vowel durations
without phonetic or orthographic transcription. Results comparing the model to
three sets of manually annotated data suggest it out-performed the current gold
standard for duration measurement, an HMM-based forced aligner (which requires
orthographic or phonetic transcription as an input).
Intelligence in Artificial Intelligence
Authors: Shoumen Palit Austin Datta
Date: 2016-10-24 00:00:00

The elusive quest for intelligence in artificial intelligence prompts us to
consider that instituting human-level intelligence in systems may be (still) in
the realm of utopia. In about a quarter century, we have witnessed the winter
of AI (1990) being transformed and transported to the zenith of tabloid fodder
about AI (2015). The discussion at hand is about the elements that constitute
the canonical idea of intelligence. The delivery of intelligence as a
pay-per-use-service, popping out of an app or from a shrink-wrapped software
defined point solution, is in contrast to the bio-inspired view of intelligence
as an outcome, perhaps formed from a tapestry of events, cross-pollinated by
instances, each with its own microcosm of experiences and learning, which may
not be discrete all-or-none functions but continuous, over space and time. The
enterprise world may not require, aspire or desire such an engaged solution to
improve its services for enabling digital transformation through the deployment
of digital twins, for example. One might ask whether the "work-flow on
steroids" version of decision support may suffice for intelligence? Are we
harking back to the era of rule based expert systems? The image conjured by the
publicity machines offers deep solutions with human-level AI and preposterous
claims about capturing the "brain in a box" by 2020. Even emulating insects may
be difficult in terms of real progress. Perhaps we can try to focus on worms
(Caenorhabditis elegans) which may be better suited for what business needs to
quench its thirst for so-called intelligence in AI.
Incremental Nonparametric Weighted Feature Extraction for OnlineSubspace Pattern Classification
Authors: Hamid Abrishami Moghaddam, Elaheh Raisi
Date: 2016-10-26 00:00:00
Tasks: Classification, General Classification

In this paper, a new online method based on nonparametric weighted feature
extraction (NWFE) is proposed. NWFE was introduced to enjoy optimum
characteristics of linear discriminant analysis (LDA) and nonparametric
discriminant analysis (NDA) while rectifying their drawbacks. It emphasizes the
points near decision boundary by putting greater weights on them and
deemphasizes other points. Incremental nonparametric weighted feature
extraction (INWFE) is the online version of NWFE. INWFE has advantages of NWFE
method such as extracting more than L-1 features in contrast to LDA. It is
independent of the class distribution and performs well in complex distributed
data. The effects of outliers are reduced due to the nature of its
nonparametric scatter matrix. Furthermore, it is possible to add new samples
asynchronously, i.e. whenever a new sample becomes available at any given time,
it can be added to the algorithm. This is useful for many real world
applications since all data cannot be available in advance. This method is
implemented on Gaussian and non-Gaussian multidimensional data, a number of UCI
datasets and Indian Pine dataset. Results are compared with NWFE in terms of
classification accuracy and execution time. For nearest neighbour classifier it
shows that this technique converges to NWFE at the end of learning process. In
addition, the computational complexity is reduced in comparison with NWFE in
terms of execution time.
Fast Bayesian Non-Negative Matrix Factorisation and Tri-Factorisation
Authors: Thomas Brouwer, Jes Frellsen, Pietro Lio'
Date: 2016-10-26 00:00:00

We present a fast variational Bayesian algorithm for performing non-negative
matrix factorisation and tri-factorisation. We show that our approach achieves
faster convergence per iteration and timestep (wall-clock) than Gibbs sampling
and non-probabilistic approaches, and do not require additional samples to
estimate the posterior. We show that in particular for matrix tri-factorisation
convergence is difficult, but our variational Bayesian approach offers a fast
solution, allowing the tri-factorisation approach to be used more effectively.
Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards
Authors: Suchet Bargoti, James Underwood
Date: 2016-10-25 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation

Ground vehicles equipped with monocular vision systems are a valuable source
of high resolution image data for precision agriculture applications in
orchards. This paper presents an image processing framework for fruit detection
and counting using orchard image data. A general purpose image segmentation
approach is used, including two feature learning algorithms; multi-scale
Multi-Layered Perceptrons (MLP) and Convolutional Neural Networks (CNN). These
networks were extended by including contextual information about how the image
data was captured (metadata), which correlates with some of the appearance
variations and/or class distributions observed in the data. The pixel-wise
fruit segmentation output is processed using the Watershed Segmentation (WS)
and Circular Hough Transform (CHT) algorithms to detect and count individual
fruits. Experiments were conducted in a commercial apple orchard near
Melbourne, Australia. The results show an improvement in fruit segmentation
performance with the inclusion of metadata on the previously benchmarked MLP
network. We extend this work with CNNs, bringing agrovision closer to the
state-of-the-art in computer vision, where although metadata had negligible
influence, the best pixel-wise F1-score of $0.791$ was achieved. The WS
algorithm produced the best apple detection and counting results, with a
detection F1-score of $0.858$. As a final step, image fruit counts were
accumulated over multiple rows at the orchard and compared against the
post-harvest fruit counts that were obtained from a grading and counting
machine. The count estimates using CNN and WS resulted in the best performance
for this dataset, with a squared correlation coefficient of $r^2=0.826$.
Indirect Gaussian Graph Learning beyond Gaussianity
Authors: Yiyuan She, Shao Tang, Qiaoya Zhang
Date: 2016-10-08 00:00:00
Tasks: Graph Learning

This paper studies how to capture dependency graph structures from real data which may not be Gaussian. Starting from marginal loss functions not necessarily derived from probability distributions, we utilize an additive over-parametrization with shrinkage to incorporate variable dependencies into the criterion. An iterative Gaussian graph learning algorithm is proposed with ease in implementation. Statistical analysis shows that the estimators achieve satisfactory accuracy with the error measured in terms of a proper Bregman divergence. Real-life examples in different settings are given to demonstrate the efficacy of the proposed methodology.
A Physician Advisory System for Chronic Heart Failure Management Based on Knowledge Patterns
Authors: Zhuo Chen, Kyle Marple, Elmer Salazar, Gopal Gupta, Lakshman Tamil
Date: 2016-10-25 00:00:00
Tasks: Management

Management of chronic diseases such as heart failure, diabetes, and chronic
obstructive pulmonary disease (COPD) is a major problem in health care. A
standard approach that the medical community has devised to manage widely
prevalent chronic diseases such as chronic heart failure (CHF) is to have a
committee of experts develop guidelines that all physicians should follow.
These guidelines typically consist of a series of complex rules that make
recommendations based on a patient's information. Due to their complexity,
often the guidelines are either ignored or not complied with at all, which can
result in poor medical practices. It is not even clear whether it is humanly
possible to follow these guidelines due to their length and complexity. In the
case of CHF management, the guidelines run nearly 80 pages. In this paper we
describe a physician-advisory system for CHF management that codes the entire
set of clinical practice guidelines for CHF using answer set programming. Our
approach is based on developing reasoning templates (that we call knowledge
patterns) and using these patterns to systemically code the clinical guidelines
for CHF as ASP rules. Use of the knowledge patterns greatly facilitates the
development of our system. Given a patient's medical information, our system
generates a recommendation for treatment just as a human physician would, using
the guidelines. Our system will work even in the presence of incomplete
information. Our work makes two contributions: (i) it shows that highly complex
guidelines can be successfully coded as ASP rules, and (ii) it develops a
series of knowledge patterns that facilitate the coding of knowledge expressed
in a natural language and that can be used for other application domains. This
paper is under consideration for acceptance in TPLP.
Selective Factor Extraction in High Dimensions
Authors: Yiyuan She
Date: 2014-03-25 00:00:00
Tasks: feature selection, Model Selection, Variable Selection, Vocal Bursts Intensity Prediction

This paper studies simultaneous feature selection and extraction in
supervised and unsupervised learning. We propose and investigate selective
reduced rank regression for constructing optimal explanatory factors from a
parsimonious subset of input features. The proposed estimators enjoy sharp
oracle inequalities, and with a predictive information criterion for model
selection, they adapt to unknown sparsity by controlling both rank and row
support of the coefficient matrix. A class of algorithms is developed that can
accommodate various convex and nonconvex sparsity-inducing penalties, and can
be used for rank-constrained variable screening in high-dimensional
multivariate data. The paper also showcases applications in macroeconomics and
computer vision to demonstrate how low-dimensional data structures can be
effectively captured by joint variable selection and projection.
A Non-convex One-Pass Framework for Generalized Factorization Machine and Rank-One Matrix Sensing
Authors: Ming Lin, Jieping Ye
Date: 2016-08-21 00:00:00
Proceeding: NeurIPS 2016 12
Tasks: Matrix Completion, Retrieval

We develop an efficient alternating framework for learning a generalized
version of Factorization Machine (gFM) on steaming data with provable
guarantees. When the instances are sampled from $d$ dimensional random Gaussian
vectors and the target second order coefficient matrix in gFM is of rank $k$,
our algorithm converges linearly, achieves $O(\epsilon)$ recovery error after
retrieving $O(k^{3}d\log(1/\epsilon))$ training instances, consumes $O(kd)$
memory in one-pass of dataset and only requires matrix-vector product
operations in each iteration. The key ingredient of our framework is a
construction of an estimation sequence endowed with a so-called Conditionally
Independent RIP condition (CI-RIP). As special cases of gFM, our framework can
be applied to symmetric or asymmetric rank-one matrix sensing problems, such as
inductive matrix completion and phase retrieval.
Modeling Ambiguity, Subjectivity, and Diverging Viewpoints in Opinion Question Answering Systems
Authors: Mengting Wan, Julian McAuley
Date: 2016-10-25 00:00:00
Tasks: Question Answering

Product review websites provide an incredible lens into the wide variety of
opinions and experiences of different people, and play a critical role in
helping users discover products that match their personal needs and
preferences. To help address questions that can't easily be answered by reading
others' reviews, some review websites also allow users to pose questions to the
community via a question-answering (QA) system. As one would expect, just as
opinions diverge among different reviewers, answers to such questions may also
be subjective, opinionated, and divergent. This means that answering such
questions automatically is quite different from traditional QA tasks, where it
is assumed that a single `correct' answer is available. While recent work
introduced the idea of question-answering using product reviews, it did not
account for two aspects that we consider in this paper: (1) Questions have
multiple, often divergent, answers, and this full spectrum of answers should
somehow be used to train the system; and (2) What makes a `good' answer depends
on the asker and the answerer, and these factors should be incorporated in
order for the system to be more personalized. Here we build a new QA dataset
with 800 thousand questions---and over 3.1 million answers---and show that
explicitly accounting for personalization and ambiguity leads both to
quantitatively better answers, but also a more nuanced view of the range of
supporting, but subjective, opinions.
Dis-S2V: Discourse Informed Sen2Vec
Authors: Tanay Kumar Saha, Shafiq Joty, Naeemul Hassan, Mohammad Al Hasan
Date: 2016-10-25 00:00:00
Tasks: Clustering, Computational Efficiency, Sentence

Vector representation of sentences is important for many text processing
tasks that involve clustering, classifying, or ranking sentences. Recently,
distributed representation of sentences learned by neural models from unlabeled
data has been shown to outperform the traditional bag-of-words representation.
However, most of these learning methods consider only the content of a sentence
and disregard the relations among sentences in a discourse by and large.
  In this paper, we propose a series of novel models for learning latent
representations of sentences (Sen2Vec) that consider the content of a sentence
as well as inter-sentence relations. We first represent the inter-sentence
relations with a language network and then use the network to induce contextual
information into the content-based Sen2Vec models. Two different approaches are
introduced to exploit the information in the network. Our first approach
retrofits (already trained) Sen2Vec vectors with respect to the network in two
different ways: (1) using the adjacency relations of a node, and (2) using a
stochastic sampling method which is more flexible in sampling neighbors of a
node. The second approach uses a regularizer to encode the information in the
network into the existing Sen2Vec model. Experimental results show that our
proposed models outperform existing methods in three fundamental information
system tasks demonstrating the effectiveness of our approach. The models
leverage the computational power of multi-core CPUs to achieve fine-grained
computational efficiency. We make our code publicly available upon acceptance.
A Conceptual Development of Quench Prediction App build on LSTM and ELQA framework
Authors: Matej Mertik, Maciej Wielgosz, Andrzej Skoczeń
Date: 2016-10-25 00:00:00
Tasks: Prediction

This article presents a development of web application for quench prediction
in \gls{te-mpe-ee} at CERN. The authors describe an ELectrical Quality
Assurance (ELQA) framework, a platform which was designed for rapid development
of web integrated data analysis applications for different analysis needed
during the hardware commissioning of the Large Hadron Collider (LHC). In second
part the article describes a research carried out with the data collected from
Quench Detection System by means of using an LSTM recurrent neural network. The
article discusses and presents a conceptual work of implementing quench
prediction application for \gls{te-mpe-ee} based on the ELQA and quench
prediction algorithm.
A statistical framework for fair predictive algorithms
Authors: Kristian Lum, James Johndrow
Date: 2016-10-25 00:00:00

Predictive modeling is increasingly being employed to assist human
decision-makers. One purported advantage of replacing human judgment with
computer models in high stakes settings-- such as sentencing, hiring, policing,
college admissions, and parole decisions-- is the perceived "neutrality" of
computers. It is argued that because computer models do not hold personal
prejudice, the predictions they produce will be equally free from prejudice.
There is growing recognition that employing algorithms does not remove the
potential for bias, and can even amplify it, since training data were
inevitably generated by a process that is itself biased. In this paper, we
provide a probabilistic definition of algorithmic bias. We propose a method to
remove bias from predictive models by removing all information regarding
protected variables from the permitted training data. Unlike previous work in
this area, our framework is general enough to accommodate arbitrary data types,
e.g. binary, continuous, etc. Motivated by models currently in use in the
criminal justice system that inform decisions on pre-trial release and
paroling, we apply our proposed method to a dataset on the criminal histories
of individuals at the time of sentencing to produce "race-neutral" predictions
of re-arrest. In the process, we demonstrate that the most common approach to
creating "race-neutral" models-- omitting race as a covariate-- still results
in racially disparate predictions. We then demonstrate that the application of
our proposed method to these data removes racial disparities from predictions
with minimal impact on predictive accuracy.
Gaussian Process Kernels for Popular State-Space Time Series Models
Authors: Alexander Grigorievskiy, Juha Karhunen
Date: 2016-10-25 00:00:00
Tasks: Gaussian Processes, regression, State Space Models, Time Series, Time Series Analysis

In this paper we investigate a link between state- space models and Gaussian
Processes (GP) for time series modeling and forecasting. In particular, several
widely used state- space models are transformed into continuous time form and
corresponding Gaussian Process kernels are derived. Experimen- tal results
demonstrate that the derived GP kernels are correct and appropriate for
Gaussian Process Regression. An experiment with a real world dataset shows that
the modeling is identical with state-space models and with the proposed GP
kernels. The considered connection allows the researchers to look at their
models from a different angle and facilitate sharing ideas between these two
different modeling approaches.