pwc-archive/papers-with-abstracts huggingface dataset:
The AGI Containment Problem
Authors: James Babcock, Janos Kramar, Roman Yampolskiy
Date: 2016-04-02 00:00:00

There is considerable uncertainty about what properties, capabilities and
motivations future AGIs will have. In some plausible scenarios, AGIs may pose
security risks arising from accidents and defects. In order to mitigate these
risks, prudent early AGI research teams will perform significant testing on
their creations before use. Unfortunately, if an AGI has human-level or greater
intelligence, testing itself may not be safe; some natural AGI goal systems
create emergent incentives for AGIs to tamper with their test environments,
make copies of themselves on the internet, or convince developers and operators
to do dangerous things. In this paper, we survey the AGI containment problem -
the question of how to build a container in which tests can be conducted safely
and reliably, even on AGIs with unknown motivations and capabilities that could
be dangerous. We identify requirements for AGI containers, available
mechanisms, and weaknesses that need to be addressed.
Demand Prediction and Placement Optimization for Electric Vehicle Charging Stations
Authors: Ragavendran Gopalakrishnan, Arpita Biswas, Alefiya Lightwala, Skanda Vasudevan, Partha Dutta, Abhishek Tripathi
Date: 2016-04-19 00:00:00
Tasks: MULTI-VIEW LEARNING

Effective placement of charging stations plays a key role in Electric Vehicle
(EV) adoption. In the placement problem, given a set of candidate sites, an
optimal subset needs to be selected with respect to the concerns of both (a)
the charging station service provider, such as the demand at the candidate
sites and the budget for deployment, and (b) the EV user, such as charging
station reachability and short waiting times at the station. This work
addresses these concerns, making the following three novel contributions: (i) a
supervised multi-view learning framework using Canonical Correlation Analysis
(CCA) for demand prediction at candidate sites, using multiple datasets such as
points of interest information, traffic density, and the historical usage at
existing charging stations; (ii) a mixed-packing-and- covering optimization
framework that models competing concerns of the service provider and EV users;
(iii) an iterative heuristic to solve these problems by alternately invoking
knapsack and set cover algorithms. The performance of the demand prediction
model and the placement optimization heuristic are evaluated using real world
data.
Learning Shallow Detection Cascades for Wearable Sensor-Based Mobile Health Applications
Authors: Hamid Dadkhahi, Nazir Saleheen, Santosh Kumar, Benjamin Marlin
Date: 2016-07-13 00:00:00

The field of mobile health aims to leverage recent advances in wearable
on-body sensing technology and smart phone computing capabilities to develop
systems that can monitor health states and deliver just-in-time adaptive
interventions. However, existing work has largely focused on analyzing
collected data in the off-line setting. In this paper, we propose a novel
approach to learning shallow detection cascades developed explicitly for use in
a real-time wearable-phone or wearable-phone-cloud systems. We apply our
approach to the problem of cigarette smoking detection from a combination of
wrist-worn actigraphy data and respiration chest band data using two and three
stage cascades.
Re-presenting a Story by Emotional Factors using Sentimental Analysis Method
Authors: Hwiyeol Jo, Yohan Moon, Jong In Kim, Jeong Ryu
Date: 2016-07-13 00:00:00
Tasks: Retrieval, Transfer Learning

Remembering an event is affected by personal emotional status. We examined
the psychological status and personal factors; depression (Center for
Epidemiological Studies - Depression, Radloff, 1977), present affective
(Positive Affective and Negative Affective Schedule, Watson et al., 1988), life
orient (Life Orient Test, Scheier & Carver, 1985), self-awareness (Core Self
Evaluation Scale, Judge et al., 2003), and social factor (Social Support,
Sarason et al., 1983) of undergraduate students (N=64) and got summaries of a
story, Chronicle of a Death Foretold (Gabriel Garcia Marquez, 1981) from them.
We implement a sentimental analysis model based on convolutional neural network
(LeCun & Bengio, 1995) to evaluate each summary. From the same vein used for
transfer learning (Pan & Yang, 2010), we collected 38,265 movie review data to
train the model and then use them to score summaries of each student. The
results of CES-D and PANAS show the relationship between emotion and memory
retrieval as follows: depressed people have shown a tendency of representing a
story more negatively, and they seemed less expressive. People with full of
emotion - high in PANAS - have retrieved their memory more expressively than
others, using more negative words then others. The contributions of this study
can be summarized as follows: First, lightening the relationship between
emotion and its effect during times of storing or retrieving a memory. Second,
suggesting objective methods to evaluate the intensity of emotion in natural
language format, using a sentimental analysis model.
Possibilistic Networks: Parameters Learning from Imprecise Data and Evaluation strategy
Authors: Maroua Haddad, Philippe Leray, Nahla Ben Amor
Date: 2016-07-13 00:00:00

There has been an ever-increasing interest in multidisciplinary research on
representing and reasoning with imperfect data. Possibilistic networks present
one of the powerful frameworks of interest for representing uncertain and
imprecise information. This paper covers the problem of their parameters
learning from imprecise datasets, i.e., containing multi-valued data. We
propose in the rst part of this paper a possibilistic networks sampling
process. In the second part, we propose a likelihood function which explores
the link between random sets theory and possibility theory. This function is
then deployed to parametrize possibilistic networks.
Sequential Cost-Sensitive Feature Acquisition
Authors: Gabriella Contardo, Ludovic Denoyer, Thierry Artières
Date: 2016-07-13 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Representation Learning

We propose a reinforcement learning based approach to tackle the
cost-sensitive learning problem where each input feature has a specific cost.
The acquisition process is handled through a stochastic policy which allows
features to be acquired in an adaptive way. The general architecture of our
approach relies on representation learning to enable performing prediction on
any partially observed sample, whatever the set of its observed features are.
The resulting model is an original mix of representation learning and of
reinforcement learning ideas. It is learned with policy gradient techniques to
minimize a budgeted inference cost. We demonstrate the effectiveness of our
proposed method with several experiments on a variety of datasets for the
sparse prediction problem where all features have the same cost, but also for
some cost-sensitive settings.
Structure of a media co-occurrence network
Authors: V. A. Traag, R. Reinanda, G. van Klinken
Date: 2014-09-05 00:00:00

Social networks have been of much interest in recent years. We here focus on
a network structure derived from co-occurrences of people in traditional
newspaper media. We find three clear deviations from what can be expected in a
random graph. First, the average degree in the empirical network is much lower
than expected, and the average weight of a link much higher than expected.
Secondly, high degree nodes attract disproportionately much weight. Thirdly,
relatively much of the weight seems to concentrate between high degree nodes.
We believe this can be explained by the fact that most people tend to co-occur
repeatedly with the same people. We create a model that replicates these
observations qualitatively based on two self-reinforcing processes: (1) more
frequently occurring persons are more likely to occur again; and (2) if two
people co-occur frequently, they are more likely to co-occur again. This
suggest that the media tends to focus on people that are already in the news,
and that they reinforce existing co-occurrences.
Causal Discovery from Subsampled Time Series Data by Constraint Optimization
Authors: Antti Hyttinen, Sergey Plis, Matti Järvisalo, Frederick Eberhardt, David Danks
Date: 2016-02-25 00:00:00
Tasks: Causal Discovery, Time Series, Time Series Analysis

This paper focuses on causal structure estimation from time series data in
which measurements are obtained at a coarser timescale than the causal
timescale of the underlying system. Previous work has shown that such
subsampling can lead to significant errors about the system's causal structure
if not properly taken into account. In this paper, we first consider the search
for the system timescale causal structures that correspond to a given
measurement timescale structure. We provide a constraint satisfaction procedure
whose computational performance is several orders of magnitude better than
previous approaches. We then consider finite-sample data as input, and propose
the first constraint optimization approach for recovering the system timescale
causal structure. This algorithm optimally recovers from possible conflicts due
to statistical errors. More generally, these advances allow for a robust and
non-parametric estimation of system timescale causal structures from subsampled
time series data.
San Francisco Crime Classification
Authors: Yehya Abouelnaga
Date: 2016-07-13 00:00:00
Tasks: Classification, Crime Prediction, General Classification

San Francisco Crime Classification is an online competition administered by
Kaggle Inc. The competition aims at predicting the future crimes based on a
given set of geographical and time-based features. In this paper, I achieved a
an accuracy that ranks at top %18, as of May 19th, 2016. I will explore the
data, and explain in details the tools I used to achieve that result.
A Multi-objective Exploratory Procedure for Regression Model Selection
Authors: Ankur Sinha, Pekka Malo, Timo Kuosmanen
Date: 2012-03-28 00:00:00
Tasks: Decision Making, Model Selection, regression, Variable Selection

Variable selection is recognized as one of the most critical steps in
statistical modeling. The problems encountered in engineering and social
sciences are commonly characterized by over-abundance of explanatory variables,
non-linearities and unknown interdependencies between the regressors. An added
difficulty is that the analysts may have little or no prior knowledge on the
relative importance of the variables. To provide a robust method for model
selection, this paper introduces the Multi-objective Genetic Algorithm for
Variable Selection (MOGA-VS) that provides the user with an optimal set of
regression models for a given data-set. The algorithm considers the regression
problem as a two objective task, and explores the Pareto-optimal (best subset)
models by preferring those models over the other which have less number of
regression coefficients and better goodness of fit. The model exploration can
be performed based on in-sample or generalization error minimization. The model
selection is proposed to be performed in two steps. First, we generate the
frontier of Pareto-optimal regression models by eliminating the dominated
models without any user intervention. Second, a decision making process is
executed which allows the user to choose the most preferred model using
visualisations and simple metrics. The method has been evaluated on a recently
published real dataset on Communities and Crime within United States.
Multiple-Instance Logistic Regression with LASSO Penalty
Authors: Ray-Bing Chen, Kuang-Hung Cheng, Sheng-Mao Chang, Shuen-Lin Jeng, Ping-Yang Chen, Chun-Hao Yang, Chi-Chun Hsia
Date: 2016-07-13 00:00:00
Tasks: regression

In this work, we consider a manufactory process which can be described by a
multiple-instance logistic regression model. In order to compute the maximum
likelihood estimation of the unknown coefficient, an expectation-maximization
algorithm is proposed, and the proposed modeling approach can be extended to
identify the important covariates by adding the coefficient penalty term into
the likelihood function. In addition to essential technical details, we
demonstrate the usefulness of the proposed method by simulations and real
examples.
Fast Sampling for Strongly Rayleigh Measures with Application to Determinantal Point Processes
Authors: Chengtao Li, Stefanie Jegelka, Suvrit Sra
Date: 2016-07-13 00:00:00
Tasks: Point Processes

In this note we consider sampling from (non-homogeneous) strongly Rayleigh
probability measures. As an important corollary, we obtain a fast mixing Markov
Chain sampler for Determinantal Point Processes.
UBL: an R package for Utility-based Learning
Authors: Paula Branco, Rita P. Ribeiro, Luis Torgo
Date: 2016-04-27 00:00:00
Tasks: Classification, General Classification, regression

This document describes the R package UBL that allows the use of several
methods for handling utility-based learning problems. Classification and
regression problems that assume non-uniform costs and/or benefits pose serious
challenges to predictive analytic tasks. In the context of meteorology,
finance, medicine, ecology, among many other, specific domain information
concerning the preference bias of the users must be taken into account to
enhance the models predictive performance. To deal with this problem, a large
number of techniques was proposed by the research community for both
classification and regression tasks. The main goal of UBL package is to
facilitate the utility-based predictive analytic task by providing a set of
methods to deal with this type of problems in the R environment. It is a
versatile tool that provides mechanisms to handle both regression and
classification (binary and multiclass) tasks. Moreover, UBL package allows the
user to specify his domain preferences, but it also provides some automatic
methods that try to infer those preference bias from the domain, considering
some common known settings.
Untrimmed Video Classification for Activity Detection: submission to ActivityNet Challenge
Authors: Gurkirt Singh, Fabio Cuzzolin
Date: 2016-07-07 00:00:00
Tasks: Action Detection, Activity Detection, Activity Recognition, Binary Classification, Classification, General Classification, Human Activity Recognition, Video Classification

Current state-of-the-art human activity recognition is focused on the
classification of temporally trimmed videos in which only one action occurs per
frame. We propose a simple, yet effective, method for the temporal detection of
activities in temporally untrimmed videos with the help of untrimmed
classification. Firstly, our model predicts the top k labels for each untrimmed
video by analysing global video-level features. Secondly, frame-level binary
classification is combined with dynamic programming to generate the temporally
trimmed activity proposals. Finally, each proposal is assigned a label based on
the global label, and scored with the score of the temporal activity proposal
and the global score. Ultimately, we show that untrimmed video classification
models can be used as stepping stone for temporal detection.
Sequential Design for Ranking Response Surfaces
Authors: Ruimeng Hu, Mike Ludkovski
Date: 2015-09-03 00:00:00
Tasks: Experimental Design, Multi-Armed Bandits

We propose and analyze sequential design methods for the problem of ranking
several response surfaces. Namely, given $L \ge 2$ response surfaces over a
continuous input space $\cal X$, the aim is to efficiently find the index of
the minimal response across the entire $\cal X$. The response surfaces are not
known and have to be noisily sampled one-at-a-time. This setting is motivated
by stochastic control applications and requires joint experimental design both
in space and response-index dimensions. To generate sequential design
heuristics we investigate stepwise uncertainty reduction approaches, as well as
sampling based on posterior classification complexity. We also make connections
between our continuous-input formulation and the discrete framework of pure
regret in multi-armed bandits. To model the response surfaces we utilize
kriging surrogates. Several numerical examples using both synthetic data and an
epidemics control problem are provided to illustrate our approach and the
efficacy of respective adaptive designs.
Natural brain-information interfaces: Recommending information by relevance inferred from human brain signals
Authors: Manuel J. A. Eugster, Tuukka Ruotsalo, Michiel M. Spapé, Oswald Barral, Niklas Ravaja, Giulio Jacucci, Samuel Kaski
Date: 2016-07-12 00:00:00
Tasks: EEG, Electroencephalogram (EEG)

Finding relevant information from large document collections such as the
World Wide Web is a common task in our daily lives. Estimation of a user's
interest or search intention is necessary to recommend and retrieve relevant
information from these collections. We introduce a brain-information interface
used for recommending information by relevance inferred directly from brain
signals. In experiments, participants were asked to read Wikipedia documents
about a selection of topics while their EEG was recorded. Based on the
prediction of word relevance, the individual's search intent was modeled and
successfully used for retrieving new, relevant documents from the whole English
Wikipedia corpus. The results show that the users' interests towards digital
content can be modeled from the brain signals evoked by reading. The introduced
brain-relevance paradigm enables the recommendation of information without any
explicit user interaction, and may be applied across diverse
information-intensive applications.
Nystrom Method for Approximating the GMM Kernel
Authors: Ping Li
Date: 2016-07-12 00:00:00
Tasks: BIG-bench Machine Learning

The GMM (generalized min-max) kernel was recently proposed (Li, 2016) as a
measure of data similarity and was demonstrated effective in machine learning
tasks. In order to use the GMM kernel for large-scale datasets, the prior work
resorted to the (generalized) consistent weighted sampling (GCWS) to convert
the GMM kernel to linear kernel. We call this approach as ``GMM-GCWS''.
  In the machine learning literature, there is a popular algorithm which we
call ``RBF-RFF''. That is, one can use the ``random Fourier features'' (RFF) to
convert the ``radial basis function'' (RBF) kernel to linear kernel. It was
empirically shown in (Li, 2016) that RBF-RFF typically requires substantially
more samples than GMM-GCWS in order to achieve comparable accuracies.
  The Nystrom method is a general tool for computing nonlinear kernels, which
again converts nonlinear kernels into linear kernels. We apply the Nystrom
method for approximating the GMM kernel, a strategy which we name as
``GMM-NYS''. In this study, our extensive experiments on a set of fairly large
datasets confirm that GMM-NYS is also a strong competitor of RBF-RFF.
A Representation Theory Perspective on Simultaneous Alignment and Classification
Authors: Roy R. Lederman, Amit Singer
Date: 2016-07-12 00:00:00
Tasks: 3D Reconstruction, Classification, General Classification

One of the difficulties in 3D reconstruction of molecules from images in
single particle Cryo-Electron Microscopy (Cryo-EM), in addition to high levels
of noise and unknown image orientations, is heterogeneity in samples: in many
cases, the samples contain a mixture of molecules, or multiple conformations of
one molecule. Many algorithms for the reconstruction of molecules from images
in heterogeneous Cryo-EM experiments are based on iterative approximations of
the molecules in a non-convex optimization that is prone to reaching suboptimal
local minima. Other algorithms require an alignment in order to perform
classification, or vice versa. The recently introduced Non-Unique Games
framework provides a representation theoretic approach to studying problems of
alignment over compact groups, and offers convex relaxations for alignment
problems which are formulated as semidefinite programs (SDPs) with certificates
of global optimality under certain circumstances. In this manuscript, we
propose to extend Non-Unique Games to the problem of simultaneous alignment and
classification with the goal of simultaneously classifying Cryo-EM images and
aligning them within their respective classes. Our proposed approach can also
be extended to the case of continuous heterogeneity.
Incomplete Pivoted QR-based Dimensionality Reduction
Authors: Amit Bermanis, Aviv Rotbart, Moshe Salhov, Amir Averbuch
Date: 2016-07-12 00:00:00
Tasks: Anomaly Detection, Collaborative Filtering, Dimensionality Reduction, General Classification

High-dimensional big data appears in many research fields such as image
recognition, biology and collaborative filtering. Often, the exploration of
such data by classic algorithms is encountered with difficulties due to `curse
of dimensionality' phenomenon. Therefore, dimensionality reduction methods are
applied to the data prior to its analysis. Many of these methods are based on
principal components analysis, which is statistically driven, namely they map
the data into a low-dimension subspace that preserves significant statistical
properties of the high-dimensional data. As a consequence, such methods do not
directly address the geometry of the data, reflected by the mutual distances
between multidimensional data point. Thus, operations such as classification,
anomaly detection or other machine learning tasks may be affected.
  This work provides a dictionary-based framework for geometrically driven data
analysis that includes dimensionality reduction, out-of-sample extension and
anomaly detection. It embeds high-dimensional data in a low-dimensional
subspace. This embedding preserves the original high-dimensional geometry of
the data up to a user-defined distortion rate. In addition, it identifies a
subset of landmark data points that constitute a dictionary for the analyzed
dataset. The dictionary enables to have a natural extension of the
low-dimensional embedding to out-of-sample data points, which gives rise to a
distortion-based criterion for anomaly detection. The suggested method is
demonstrated on synthetic and real-world datasets and achieves good results for
classification, anomaly detection and out-of-sample tasks.
Reliable Prediction Intervals for Local Linear Regression
Authors: Mohammad Ghasemi Hamed, Masoud Ebadi Kivaj
Date: 2016-03-17 00:00:00
Tasks: Prediction, Prediction Intervals, regression

This paper introduces two methods for estimating reliable prediction
intervals for local linear least-squares regressions, named Bounded Oscillation
Prediction Intervals (BOPI). It also proposes a new measure for comparing
interval prediction models named Equivalent Gaussian Standard Deviation (EGSD).
The experimental results compare BOPI to other methods using coverage
probability, Mean Interval Size and the introduced EGSD measure. The results
were generally in favor of the BOPI on considered benchmark regression
datasets. It also, reports simulation studies validating the BOPI method's
reliability.
Bidirectional Recurrent Neural Networks for Medical Event Detection in Electronic Health Records
Authors: Abhyuday Jagannatha, Hong Yu
Date: 2016-06-25 00:00:00
Tasks: BIG-bench Machine Learning, Event Detection, Pharmacovigilance

Sequence labeling for extraction of medical events and their attributes from
unstructured text in Electronic Health Record (EHR) notes is a key step towards
semantic understanding of EHRs. It has important applications in health
informatics including pharmacovigilance and drug surveillance. The state of the
art supervised machine learning models in this domain are based on Conditional
Random Fields (CRFs) with features calculated from fixed context windows. In
this application, we explored various recurrent neural network frameworks and
show that they significantly outperformed the CRF models.