pwc-archive/papers-with-abstracts huggingface dataset:
Autonomous Vehicle Speed Control for Safe Navigation of Occluded Pedestrian Crosswalk
Authors: Sarah Thornton
Date: 2018-02-18 00:00:00

Both humans and the sensors on an autonomous vehicle have limited sensing
capabilities. When these limitations coincide with scenarios involving
vulnerable road users, it becomes important to account for these limitations in
the motion planner. For the scenario of an occluded pedestrian crosswalk, the
speed of the approaching vehicle should be a function of the amount of
uncertainty on the roadway. In this work, the longitudinal controller is
formulated as a partially observable Markov decision process and dynamic
programming is used to compute the control policy. The control policy scales
the speed profile to be used by a model predictive steering controller.
Restricted Eigenvalue from Stable Rank with Applications to Sparse Linear Regression
Authors: Shiva Prasad Kasiviswanathan, Mark Rudelson
Date: 2017-07-25 00:00:00
Tasks: regression

High-dimensional settings, where the data dimension ($d$) far exceeds the
number of observations ($n$), are common in many statistical and machine
learning applications. Methods based on $\ell_1$-relaxation, such as Lasso, are
very popular for sparse recovery in these settings. Restricted Eigenvalue (RE)
condition is among the weakest, and hence the most general, condition in
literature imposed on the Gram matrix that guarantees nice statistical
properties for the Lasso estimator. It is natural to ask: what families of
matrices satisfy the RE condition? Following a line of work in this area, we
construct a new broad ensemble of dependent random design matrices that have an
explicit RE bound. Our construction starts with a fixed (deterministic) matrix
$X \in \mathbb{R}^{n \times d}$ satisfying a simple stable rank condition, and
we show that a matrix drawn from the distribution $X \Phi^\top \Phi$, where
$\Phi \in \mathbb{R}^{m \times d}$ is a subgaussian random matrix, with high
probability, satisfies the RE condition. This construction allows incorporating
a fixed matrix that has an easily {\em verifiable} condition into the design
process, and allows for generation of {\em compressed} design matrices that
have a lower storage requirement than a standard design matrix. We give two
applications of this construction to sparse linear regression problems,
including one to a compressed sparse regression setting where the regression
algorithm only has access to a compressed representation of a fixed design
matrix $X$.
Learning Adversarially Fair and Transferable Representations
Authors: David Madras, Elliot Creager, Toniann Pitassi, Richard Zemel
Date: 2018-02-17 00:00:00
Proceeding: ICML 2018 7
Tasks: Fairness, Representation Learning, Transfer Learning

In this paper, we advocate for representation learning as the key to
mitigating unfair prediction outcomes downstream. Motivated by a scenario where
learned representations are used by third parties with unknown objectives, we
propose and explore adversarial representation learning as a natural method of
ensuring those parties act fairly. We connect group fairness (demographic
parity, equalized odds, and equal opportunity) to different adversarial
objectives. Through worst-case theoretical guarantees and experimental
validation, we show that the choice of this objective is crucial to fair
prediction. Furthermore, we present the first in-depth experimental
demonstration of fair transfer learning and demonstrate empirically that our
learned representations admit fair predictions on new tasks while maintaining
utility, an essential goal of fair representation learning.
Nonparametric Testing under Random Projection
Authors: Meimei Liu, Zuofeng Shang, Guang Cheng
Date: 2018-02-17 00:00:00
Tasks: regression

A common challenge in nonparametric inference is its high computational
complexity when data volume is large. In this paper, we develop computationally
efficient nonparametric testing by employing a random projection strategy. In
the specific kernel ridge regression setup, a simple distance-based test
statistic is proposed. Notably, we derive the minimum number of random
projections that is sufficient for achieving testing optimality in terms of the
minimax rate. An adaptive testing procedure is further established without
prior knowledge of regularity. One technical contribution is to establish upper
bounds for a range of tail sums of empirical kernel eigenvalues. Simulations
and real data analysis are conducted to support our theory.
Out-of-sample extension of graph adjacency spectral embedding
Authors: Keith Levin, Farbod Roosta-Khorasani, Michael W. Mahoney, Carey E. Priebe
Date: 2018-02-17 00:00:00
Proceeding: ICML 2018 7
Tasks: Dimensionality Reduction, Position

Many popular dimensionality reduction procedures have out-of-sample
extensions, which allow a practitioner to apply a learned embedding to
observations not seen in the initial training sample. In this work, we consider
the problem of obtaining an out-of-sample extension for the adjacency spectral
embedding, a procedure for embedding the vertices of a graph into Euclidean
space. We present two different approaches to this problem, one based on a
least-squares objective and the other based on a maximum-likelihood
formulation. We show that if the graph of interest is drawn according to a
certain latent position model called a random dot product graph, then both of
these out-of-sample extensions estimate the true latent position of the
out-of-sample vertex with the same error rate. Further, we prove a central
limit theorem for the least-squares-based extension, showing that the estimate
is asymptotically normal about the truth in the large-graph limit.
Machine learning for Internet of Things data analysis: A survey
Authors: Mohammad Saeid Mahdavinejad, Mohammadreza Rezvan, Mohammadamin Barekatain, Peyman Adibi, Payam Barnaghi, Amit P. Sheth
Date: 2018-02-17 00:00:00
Tasks: BIG-bench Machine Learning, Survey

Rapid developments in hardware, software, and communication technologies have
allowed the emergence of Internet-connected sensory devices that provide
observation and data measurement from the physical world. By 2020, it is
estimated that the total number of Internet-connected devices being used will
be between 25 and 50 billion. As the numbers grow and technologies become more
mature, the volume of data published will increase. Internet-connected devices
technology, referred to as Internet of Things (IoT), continues to extend the
current Internet by providing connectivity and interaction between the physical
and cyber worlds. In addition to increased volume, the IoT generates Big Data
characterized by velocity in terms of time and location dependency, with a
variety of multiple modalities and varying data quality. Intelligent processing
and analysis of this Big Data is the key to developing smart IoT applications.
This article assesses the different machine learning methods that deal with the
challenges in IoT data by considering smart cities as the main use case. The
key contribution of this study is presentation of a taxonomy of machine
learning algorithms explaining how different techniques are applied to the data
in order to extract higher level information. The potential and challenges of
machine learning for IoT data analytics will also be discussed. A use case of
applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is
presented for a more detailed exploration.
Black-Box Reductions for Parameter-free Online Learning in Banach Spaces
Authors: Ashok Cutkosky, Francesco Orabona
Date: 2018-02-17 00:00:00

We introduce several new black-box reductions that significantly improve the
design of adaptive and parameter-free online learning algorithms by simplifying
analysis, improving regret guarantees, and sometimes even improving runtime. We
reduce parameter-free online learning to online exp-concave optimization, we
reduce optimization in a Banach space to one-dimensional optimization, and we
reduce optimization over a constrained domain to unconstrained optimization.
All of our reductions run as fast as online gradient descent. We use our new
techniques to improve upon the previously best regret bounds for parameter-free
learning, and do so for arbitrary norms.
Implementation of Neural Network and feature extraction to classify ECG signals
Authors: R Karthik, Dhruv Tyagi, Amogh Raut, Soumya Saxena, Rajesh Kumar M
Date: 2018-02-17 00:00:00
Tasks: Classification, Electrocardiography (ECG), General Classification

This paper presents a suitable and efficient implementation of a feature
extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG)
signals, for detection and classification of four cardiac diseases: Sleep
Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial
Fibrillation (AF) and differentiating them from the normal heart beat by using
pan Tompkins RR detection followed by feature extraction for classification
purpose .The paper also presents a new approach towards signal classification
using the existing neural networks classifiers.
Unsupervised vehicle recognition using incremental reseeding of acoustic signatures
Authors: Justin Sunu, Blake Hunter, Allon G. Percus
Date: 2018-02-17 00:00:00
Tasks: Clustering, Dimensionality Reduction, General Classification, Management

Vehicle recognition and classification have broad applications, ranging from
traffic flow management to military target identification. We demonstrate an
unsupervised method for automated identification of moving vehicles from
roadside audio sensors. Using a short-time Fourier transform to decompose audio
signals, we treat the frequency signature in each time window as an individual
data point. We then use a spectral embedding for dimensionality reduction.
Based on the leading eigenvectors, we relate the performance of an incremental
reseeding algorithm to that of spectral clustering. We find that incremental
reseeding accurately identifies individual vehicles using their acoustic
signatures.
Nonconvex Matrix Factorization from Rank-One Measurements
Authors: Yuanxin Li, Cong Ma, Yuxin Chen, Yuejie Chi
Date: 2018-02-17 00:00:00
Tasks: Polynomial Neural Networks, Quantum State Tomography, Retrieval

We consider the problem of recovering low-rank matrices from random rank-one
measurements, which spans numerous applications including covariance sketching,
phase retrieval, quantum state tomography, and learning shallow polynomial
neural networks, among others. Our approach is to directly estimate the
low-rank factor by minimizing a nonconvex quadratic loss function via vanilla
gradient descent, following a tailored spectral initialization. When the true
rank is small, this algorithm is guaranteed to converge to the ground truth (up
to global ambiguity) with near-optimal sample complexity and computational
complexity. To the best of our knowledge, this is the first guarantee that
achieves near-optimality in both metrics. In particular, the key enabler of
near-optimal computational guarantees is an implicit regularization phenomenon:
without explicit regularization, both spectral initialization and the gradient
descent iterates automatically stay within a region incoherent with the
measurement vectors. This feature allows one to employ much more aggressive
step sizes compared with the ones suggested in prior literature, without the
need of sample splitting.
Dimensionality reduction for acoustic vehicle classification with spectral embedding
Authors: Justin Sunu, Allon G. Percus
Date: 2017-05-27 00:00:00
Tasks: Classification, Dimensionality Reduction, General Classification

We propose a method for recognizing moving vehicles, using data from roadside
audio sensors. This problem has applications ranging widely, from traffic
analysis to surveillance. We extract a frequency signature from the audio
signal using a short-time Fourier transform, and treat each time window as an
individual data point to be classified. By applying a spectral embedding, we
decrease the dimensionality of the data sufficiently for K-nearest neighbors to
provide accurate vehicle identification.
Nearly Optimal Adaptive Procedure with Change Detection for Piecewise-Stationary Bandit
Authors: Yang Cao, Zheng Wen, Branislav Kveton, Yao Xie
Date: 2018-02-11 00:00:00
Tasks: Change Detection

Multi-armed bandit (MAB) is a class of online learning problems where a
learning agent aims to maximize its expected cumulative reward while repeatedly
selecting to pull arms with unknown reward distributions. We consider a
scenario where the reward distributions may change in a piecewise-stationary
fashion at unknown time steps. We show that by incorporating a simple
change-detection component with classic UCB algorithms to detect and adapt to
changes, our so-called M-UCB algorithm can achieve nearly optimal regret bound
on the order of $O(\sqrt{MKT\log T})$, where $T$ is the number of time steps,
$K$ is the number of arms, and $M$ is the number of stationary segments.
Comparison with the best available lower bound shows that our M-UCB is nearly
optimal in $T$ up to a logarithmic factor. We also compare M-UCB with the
state-of-the-art algorithms in numerical experiments using a public Yahoo!
dataset to demonstrate its superior performance.
An analysis of training and generalization errors in shallow and deep networks
Authors: Hrushikesh Mhaskar, Tomaso Poggio
Date: 2018-02-17 00:00:00

This paper is motivated by an open problem around deep networks, namely, the apparent absence of over-fitting despite large over-parametrization which allows perfect fitting of the training data. In this paper, we analyze this phenomenon in the case of regression problems when each unit evaluates a periodic activation function. We argue that the minimal expected value of the square loss is inappropriate to measure the generalization error in approximation of compositional functions in order to take full advantage of the compositional structure. Instead, we measure the generalization error in the sense of maximum loss, and sometimes, as a pointwise error. We give estimates on exactly how many parameters ensure both zero training error as well as a good generalization error. We prove that a solution of a regularization problem is guaranteed to yield a good training error as well as a good generalization error and estimate how much error to expect at which test data.
Exact and Consistent Interpretation for Piecewise Linear Neural Networks: A Closed Form Solution
Authors: Lingyang Chu, Xia Hu, Juhua Hu, Lanjun Wang, Jian Pei
Date: 2018-02-17 00:00:00
Tasks: Form

Strong intelligent machines powered by deep neural networks are increasingly deployed as black boxes to make decisions in risk-sensitive domains, such as finance and medical. To reduce potential risk and build trust with users, it is critical to interpret how such machines make their decisions. Existing works interpret a pre-trained neural network by analyzing hidden neurons, mimicking pre-trained models or approximating local predictions. However, these methods do not provide a guarantee on the exactness and consistency of their interpretation. In this paper, we propose an elegant closed form solution named $OpenBox$ to compute exact and consistent interpretations for the family of Piecewise Linear Neural Networks (PLNN). The major idea is to first transform a PLNN into a mathematically equivalent set of linear classifiers, then interpret each linear classifier by the features that dominate its prediction. We further apply $OpenBox$ to demonstrate the effectiveness of non-negative and sparse constraints on improving the interpretability of PLNNs. The extensive experiments on both synthetic and real world data sets clearly demonstrate the exactness and consistency of our interpretation.
MIDA: Multiple Imputation using Denoising Autoencoders
Authors: Lovedeep Gondara, Ke Wang
Date: 2017-05-08 00:00:00
Tasks: Denoising, Imputation

Missing data is a significant problem impacting all domains. State-of-the-art
framework for minimizing missing data bias is multiple imputation, for which
the choice of an imputation model remains nontrivial. We propose a multiple
imputation model based on overcomplete deep denoising autoencoders. Our
proposed model is capable of handling different data types, missingness
patterns, missingness proportions and distributions. Evaluation on several real
life datasets show our proposed model significantly outperforms current
state-of-the-art methods under varying conditions while simultaneously
improving end of the line analytics.
Learning Analytics in Massive Open Online Courses
Authors: Mohammad Khalil
Date: 2018-02-17 00:00:00

Educational technology has obtained great importance over the last fifteen
years. At present, the umbrella of educational technology incorporates
multitudes of engaging online environments and fields. Learning analytics and
Massive Open Online Courses (MOOCs) are two of the most relevant emerging
topics in this domain. Since they are open to everyone at no cost, MOOCs excel
in attracting numerous participants that can reach hundreds and hundreds of
thousands. Experts from different disciplines have shown significant interest
in MOOCs as the phenomenon has rapidly grown. In fact, MOOCs have been proven
to scale education in disparate areas. Their benefits are crystallized in the
improvement of educational outcomes, reduction of costs and accessibility
expansion. Due to their unusual massiveness, the large datasets of MOOC
platforms require advanced tools and methodologies for further examination. The
key importance of learning analytics is reflected here. MOOCs offer diverse
challenges and practices for learning analytics to tackle. In view of that,
this thesis combines both fields in order to investigate further steps in the
learning analytics capabilities in MOOCs. The primary research of this
dissertation focuses on the integration of learning analytics in MOOCs, and
thereafter looks into examining students' behavior on one side and bridging
MOOC issues on the other side. The research was done on the Austrian iMooX
xMOOC platform. We followed the prototyping and case studies research
methodology to carry out the research questions of this dissertation. The main
contributions incorporate designing a general learning analytics framework,
learning analytics prototype, records of students' behavior in nearly every
MOOC's variables (discussion forums, interactions in videos, self-assessment
quizzes, login frequency), a cluster of student engagement...
Global-scale phylogenetic linguistic inference from lexical resources
Authors: Gerhard Jäger
Date: 2018-02-17 00:00:00
Tasks: Clustering, Diversity

Automatic phylogenetic inference plays an increasingly important role in
computational historical linguistics. Most pertinent work is currently based on
expert cognate judgments. This limits the scope of this approach to a small
number of well-studied language families. We used machine learning techniques
to compile data suitable for phylogenetic inference from the ASJP database, a
collection of almost 7,000 phonetically transcribed word lists over 40
concepts, covering two third of the extant world-wide linguistic diversity.
First, we estimated Pointwise Mutual Information scores between sound classes
using weighted sequence alignment and general-purpose optimization. From this
we computed a dissimilarity matrix over all ASJP word lists. This matrix is
suitable for distance-based phylogenetic inference. Second, we applied cognate
clustering to the ASJP data, using supervised training of an SVM classifier on
expert cognacy judgments. Third, we defined two types of binary characters,
based on automatically inferred cognate classes and on sound-class occurrences.
Several tests are reported demonstrating the suitability of these characters
for character-based phylogenetic inference.
Connectivity-Driven Parcellation Methods for the Human Cerebral Cortex
Authors: Salim Arslan
Date: 2018-02-17 00:00:00
Tasks: Clustering, Image Segmentation, Semantic Segmentation

In this thesis, we present robust and fully-automated methods for the
subdivision of the entire human cerebral cortex based on connectivity
information. Our contributions are four-fold: First, we propose a clustering
approach to delineate a cortical parcellation that provides a reliable
abstraction of the brain's functional organisation. Second, we cast the
parcellation problem as a feature reduction problem and make use of manifold
learning and image segmentation techniques to identify cortical regions with
distinct structural connectivity patterns. Third, we present a multi-layer
graphical model that combines within- and between-subject connectivity, which
is then decomposed into a cortical parcellation that can represent the whole
population, while accounting for the variability across subjects. Finally, we
conduct a large-scale, systematic comparison of existing parcellation methods,
with a focus on providing some insight into the reliability of brain
parcellations in terms of reflecting the underlying connectivity, as well as,
revealing their impact on network analysis.
  We evaluate the proposed parcellation methods on publicly available data from
the Human Connectome Project and a plethora of quantitative and qualitative
evaluation techniques investigated in the literature. Experiments across
multiple resolutions demonstrate the accuracy of the presented methods at both
subject and group levels with regards to reproducibility and fidelity to the
data. The neuro-biological interpretation of the proposed parcellations is also
investigated by comparing parcel boundaries with well-structured properties of
the cerebral cortex. Results show the advantage of connectivity-driven
parcellations over traditional approaches in terms of better fitting the
underlying connectivity.
Bayes-optimal Hierarchical Classification over Asymmetric Tree-Distance Loss
Authors: Dheeraj Mekala, Vivek Gupta, Purushottam Kar, Harish Karnick
Date: 2018-02-17 00:00:00
Tasks: Classification, General Classification, Multi-class Classification

Hierarchical classification is supervised multi-class classification problem
over the set of class labels organized according to a hierarchy. In this
report, we study the work by Ramaswamy et. al. on hierarchical classification
over symmetric tree distance loss. We extend the consistency of hierarchical
classification algorithm over asymmetric tree distance loss. We design a
$\mathcal{O}(nk\log{}n)$ algorithm to find Bayes optimal classification for a
k-ary tree as a hierarchy. We show that under reasonable assumptions over
asymmetric loss function, the Bayes optimal classification over this asymmetric
loss can be found in $\mathcal{O}(k\log{}n)$. We exploit this insight and
attempt to extend the Ova-Cascade algorithm \citet{ramaswamy2015convex} for
hierarchical classification over the asymmetric loss.