pwc-archive/papers-with-abstracts huggingface dataset:
Fair Forests: Regularized Tree Induction to Minimize Model Bias
Authors: Edward Raff, Jared Sylvester, Steven Mills
Date: 2017-12-21 00:00:00
Tasks: Attribute, Fairness

The potential lack of fairness in the outputs of machine learning algorithms
has recently gained attention both within the research community as well as in
society more broadly. Surprisingly, there is no prior work developing
tree-induction algorithms for building fair decision trees or fair random
forests. These methods have widespread popularity as they are one of the few to
be simultaneously interpretable, non-linear, and easy-to-use. In this paper we
develop, to our knowledge, the first technique for the induction of fair
decision trees. We show that our "Fair Forest" retains the benefits of the
tree-based approach, while providing both greater accuracy and fairness than
other alternatives, for both "group fairness" and "individual fairness.'" We
also introduce new measures for fairness which are able to handle multinomial
and continues attributes as well as regression problems, as opposed to binary
attributes and labels only. Finally, we demonstrate a new, more robust
evaluation procedure for algorithms that considers the dataset in its entirety
rather than only a specific protected attribute.
Deep Appearance Models: A Deep Boltzmann Machine Approach for Face Modeling
Authors: Chi Nhan Duong, Khoa Luu, Kha Gia Quach, Tien D. Bui
Date: 2016-07-23 00:00:00
Tasks: Age Estimation, Super-Resolution

The "interpretation through synthesis" approach to analyze face images,
particularly Active Appearance Models (AAMs) method, has become one of the most
successful face modeling approaches over the last two decades. AAM models have
ability to represent face images through synthesis using a controllable
parameterized Principal Component Analysis (PCA) model. However, the accuracy
and robustness of the synthesized faces of AAM are highly depended on the
training sets and inherently on the generalizability of PCA subspaces. This
paper presents a novel Deep Appearance Models (DAMs) approach, an efficient
replacement for AAMs, to accurately capture both shape and texture of face
images under large variations. In this approach, three crucial components
represented in hierarchical layers are modeled using the Deep Boltzmann
Machines (DBM) to robustly capture the variations of facial shapes and
appearances. DAMs are therefore superior to AAMs in inferencing a
representation for new face images under various challenging conditions. The
proposed approach is evaluated in various applications to demonstrate its
robustness and capabilities, i.e. facial super-resolution reconstruction,
facial off-angle reconstruction or face frontalization, facial occlusion
removal and age estimation using challenging face databases, i.e. Labeled Face
Parts in the Wild (LFPW), Helen and FG-NET. Comparing to AAMs and other deep
learning based approaches, the proposed DAMs achieve competitive results in
those applications, thus this showed their advantages in handling occlusions,
facial representation, and reconstruction.
The FastMap Algorithm for Shortest Path Computations
Authors: Liron Cohen, Tansel Uras, Shiva Jahangiri, Aliyah Arunasalam, Sven Koenig, T. K. Satish Kumar
Date: 2017-06-08 00:00:00

We present a new preprocessing algorithm for embedding the nodes of a given
edge-weighted undirected graph into a Euclidean space. The Euclidean distance
between any two nodes in this space approximates the length of the shortest
path between them in the given graph. Later, at runtime, a shortest path
between any two nodes can be computed with A* search using the Euclidean
distances as heuristic. Our preprocessing algorithm, called FastMap, is
inspired by the data mining algorithm of the same name and runs in near-linear
time. Hence, FastMap is orders of magnitude faster than competing approaches
that produce a Euclidean embedding using Semidefinite Programming. FastMap also
produces admissible and consistent heuristics and therefore guarantees the
generation of shortest paths. Moreover, FastMap applies to general undirected
graphs for which many traditional heuristics, such as the Manhattan Distance
heuristic, are not well defined. Empirically, we demonstrate that A* search
using the FastMap heuristic is competitive with A* search using other
state-of-the-art heuristics, such as the Differential heuristic.
A Convex Program for Mixed Linear Regression with a Recovery Guarantee for Well-Separated Data
Authors: Paul Hand, Babhru Joshi
Date: 2016-12-19 00:00:00
Tasks: regression

We introduce a convex approach for mixed linear regression over $d$ features.
This approach is a second-order cone program, based on L1 minimization, which
assigns an estimate regression coefficient in $\mathbb{R}^{d}$ for each data
point. These estimates can then be clustered using, for example, $k$-means. For
problems with two or more mixture classes, we prove that the convex program
exactly recovers all of the mixture components in the noiseless setting under
technical conditions that include a well-separation assumption on the data.
Under these assumptions, recovery is possible if each class has at least $d$
independent measurements. We also explore an iteratively reweighted least
squares implementation of this method on real and synthetic data.
Unifying Map and Landmark Based Representations for Visual Navigation
Authors: Saurabh Gupta, David Fouhey, Sergey Levine, Jitendra Malik
Date: 2017-12-21 00:00:00
Tasks: Navigate, Spatial Reasoning, Visual Navigation

This works presents a formulation for visual navigation that unifies map
based spatial reasoning and path planning, with landmark based robust plan
execution in noisy environments. Our proposed formulation is learned from data
and is thus able to leverage statistical regularities of the world. This allows
it to efficiently navigate in novel environments given only a sparse set of
registered images as input for building representations for space. Our
formulation is based on three key ideas: a learned path planner that outputs
path plans to reach the goal, a feature synthesis engine that predicts features
for locations along the planned path, and a learned goal-driven closed loop
controller that can follow plans given these synthesized features. We test our
approach for goal-driven navigation in simulated real world environments and
report performance gains over competitive baseline approaches.
Non-Stationary Bandits with Habituation and Recovery Dynamics
Authors: Yonatan Mintz, Anil Aswani, Philip Kaminsky, Elena Flowers, Yoshimi Fukuoka
Date: 2017-07-26 00:00:00
Tasks: Decision Making, Sequential Decision Making

Many settings involve sequential decision-making where a set of actions can be chosen at each time step, each action provides a stochastic reward, and the distribution for the reward of each action is initially unknown. However, frequent selection of a specific action may reduce its expected reward, while abstaining from choosing an action may cause its expected reward to increase. Such non-stationary phenomena are observed in many real world settings such as personalized healthcare-adherence improving interventions and targeted online advertising. Though finding an optimal policy for general models with non-stationarity is PSPACE-complete, we propose and analyze a new class of models called ROGUE (Reducing or Gaining Unknown Efficacy) bandits, which we show in this paper can capture these phenomena and are amenable to the design of effective policies. We first present a consistent maximum likelihood estimator for the parameters of these models. Next, we construct finite sample concentration bounds that lead to an upper confidence bound policy called the ROGUE Upper Confidence Bound (ROGUE-UCB) algorithm. We prove that under proper conditions the ROGUE-UCB algorithm achieves logarithmic in time regret, unlike existing algorithms which result in linear regret. We conclude with a numerical experiment using real data from a personalized healthcare-adherence improving intervention to increase physical activity. In this intervention, the goal is to optimize the selection of messages (e.g., confidence increasing vs. knowledge increasing) to send to each individual each day to increase adherence and physical activity. Our results show that ROGUE-UCB performs better in terms of regret and average reward as compared to state of the art algorithms, and the use of ROGUE-UCB increases daily step counts by roughly 1,000 steps a day (about a half-mile more of walking) as compared to other algorithms.
A Deep Learning Interpretable Classifier for Diabetic Retinopathy Disease Grading
Authors: Jordi de la Torre, Aida Valls, Domenec Puig
Date: 2017-12-21 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification, Medical Diagnosis

Deep neural network models have been proven to be very successful in image
classification tasks, also for medical diagnosis, but their main concern is its
lack of interpretability. They use to work as intuition machines with high
statistical confidence but unable to give interpretable explanations about the
reported results. The vast amount of parameters of these models make difficult
to infer a rationale interpretation from them. In this paper we present a
diabetic retinopathy interpretable classifier able to classify retine images
into the different levels of disease severity and of explaining its results by
assigning a score for every point in the hidden and input space, evaluating its
contribution to the final classification in a linear way. The generated visual
maps can be interpreted by an expert in order to compare its own knowledge with
the interpretation given by the model.
Profit Driven Decision Trees for Churn Prediction
Authors: Sebastiaan Höppner, Eugen Stripling, Bart Baesens, Seppe vanden Broucke, Tim Verdonck
Date: 2017-12-21 00:00:00
Tasks: Binary Classification, General Classification, Prediction

Customer retention campaigns increasingly rely on predictive models to detect
potential churners in a vast customer base. From the perspective of machine
learning, the task of predicting customer churn can be presented as a binary
classification problem. Using data on historic behavior, classification
algorithms are built with the purpose of accurately predicting the probability
of a customer defecting. The predictive churn models are then commonly selected
based on accuracy related performance measures such as the area under the ROC
curve (AUC). However, these models are often not well aligned with the core
business requirement of profit maximization, in the sense that, the models fail
to take into account not only misclassification costs, but also the benefits
originating from a correct classification. Therefore, the aim is to construct
churn prediction models that are profitable and preferably interpretable too.
The recently developed expected maximum profit measure for customer churn
(EMPC) has been proposed in order to select the most profitable churn model. We
present a new classifier that integrates the EMPC metric directly into the
model construction. Our technique, called ProfTree, uses an evolutionary
algorithm for learning profit driven decision trees. In a benchmark study with
real-life data sets from various telecommunication service providers, we show
that ProfTree achieves significant profit improvements compared to classic
accuracy driven tree-based methods.
Multiview Deep Learning for Predicting Twitter Users' Location
Authors: Tien Huu Do, Duc Minh Nguyen, Evaggelia Tsiligianni, Bruno Cornelis, Nikos Deligiannis
Date: 2017-12-21 00:00:00
Tasks: Deep Learning, Marketing, Multiview Learning

The problem of predicting the location of users on large social networks like
Twitter has emerged from real-life applications such as social unrest detection
and online marketing. Twitter user geolocation is a difficult and active
research topic with a vast literature. Most of the proposed methods follow
either a content-based or a network-based approach. The former exploits
user-generated content while the latter utilizes the connection or interaction
between Twitter users. In this paper, we introduce a novel method combining the
strength of both approaches. Concretely, we propose a multi-entry neural
network architecture named MENET leveraging the advances in deep learning and
multiview learning. The generalizability of MENET enables the integration of
multiple data representations. In the context of Twitter user geolocation, we
realize MENET with textual, network, and metadata features. Considering the
natural distribution of Twitter users across the concerned geographical area,
we subdivide the surface of the earth into multi-scale cells and train MENET
with the labels of the cells. We show that our method outperforms the state of
the art by a large margin on three benchmark datasets.
Learning to Look Around: Intelligently Exploring Unseen Environments for Unknown Tasks
Authors: Dinesh Jayaraman, Kristen Grauman
Date: 2017-09-01 00:00:00
Proceeding: CVPR 2018 6
Tasks: Reinforcement Learning

It is common to implicitly assume access to intelligently captured inputs
(e.g., photos from a human photographer), yet autonomously capturing good
observations is itself a major challenge. We address the problem of learning to
look around: if a visual agent has the ability to voluntarily acquire new views
to observe its environment, how can it learn efficient exploratory behaviors to
acquire informative observations? We propose a reinforcement learning solution,
where the agent is rewarded for actions that reduce its uncertainty about the
unobserved portions of its environment. Based on this principle, we develop a
recurrent neural network-based approach to perform active completion of
panoramic natural scenes and 3D object shapes. Crucially, the learned policies
are not tied to any recognition task nor to the particular semantic content
seen during training. As a result, 1) the learned "look around" behavior is
relevant even for new tasks in unseen environments, and 2) training data
acquisition involves no manual labeling. Through tests in diverse settings, we
demonstrate that our approach learns useful generic policies that transfer to
new unseen tasks and environments. Completion episodes are shown at
https://goo.gl/BgWX3W.
AVEID: Automatic Video System for Measuring Engagement In Dementia
Authors: Viral Parekh, Pin Sym Foong, Shendong Zhao, Ramanathan Subramanian
Date: 2017-12-21 00:00:00

Engagement in dementia is typically measured using behavior observational
scales (BOS) that are tedious and involve intensive manual labor to annotate,
and are therefore not easily scalable. We propose AVEID, a low cost and
easy-to-use video-based engagement measurement tool to determine the engagement
level of a person with dementia (PwD) during digital interaction. We show that
the objective behavioral measures computed via AVEID correlate well with
subjective expert impressions for the popular MPES and OME BOS, confirming its
viability and effectiveness. Moreover, AVEID measures can be obtained for a
variety of engagement designs, thereby facilitating large-scale studies with
PwD populations.
Note on Attacking Object Detectors with Adversarial Stickers
Authors: Kevin Eykholt, Ivan Evtimov, Earlence Fernandes, Bo Li, Dawn Song, Tadayoshi Kohno, Amir Rahmati, Atul Prakash, Florian Tramer
Date: 2017-12-21 00:00:00
Tasks: Deep Learning, Object

Deep learning has proven to be a powerful tool for computer vision and has
seen widespread adoption for numerous tasks. However, deep learning algorithms
are known to be vulnerable to adversarial examples. These adversarial inputs
are created such that, when provided to a deep learning algorithm, they are
very likely to be mislabeled. This can be problematic when deep learning is
used to assist in safety critical decisions. Recent research has shown that
classifiers can be attacked by physical adversarial examples under various
physical conditions. Given the fact that state-of-the-art objection detection
algorithms are harder to be fooled by the same set of adversarial examples,
here we show that these detectors can also be attacked by physical adversarial
examples. In this note, we briefly show both static and dynamic test results.
We design an algorithm that produces physical adversarial inputs, which can
fool the YOLO object detector and can also attack Faster-RCNN with relatively
high success rate based on transferability. Furthermore, our algorithm can
compress the size of the adversarial inputs to stickers that, when attached to
the targeted object, result in the detector either mislabeling or not detecting
the object a high percentage of the time. This note provides a small set of
results. Our upcoming paper will contain a thorough evaluation on other object
detectors, and will present the algorithm.
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution
Authors: Topi Paananen, Juho Piironen, Michael Riis Andersen, Aki Vehtari
Date: 2017-12-21 00:00:00
Tasks: Gaussian Processes, Sensitivity, Variable Selection

Variable selection for Gaussian process models is often done using automatic
relevance determination, which uses the inverse length-scale parameter of each
input variable as a proxy for variable relevance. This implicitly determined
relevance has several drawbacks that prevent the selection of optimal input
variables in terms of predictive performance. To improve on this, we propose
two novel variable selection methods for Gaussian process models that utilize
the predictions of a full model in the vicinity of the training points and
thereby rank the variables based on their predictive relevance. Our empirical
results on synthetic and real world data sets demonstrate improved variable
selection compared to automatic relevance determination in terms of variability
and predictive performance.
Language Generation with Recurrent Generative Adversarial Networks without Pre-training
Authors: Ofir Press, Amir Bar, Ben Bogin, Jonathan Berant, Lior Wolf
Date: 2017-06-05 00:00:00
Tasks: Text Generation

Generative Adversarial Networks (GANs) have shown great promise recently in
image generation. Training GANs for language generation has proven to be more
difficult, because of the non-differentiable nature of generating text with
recurrent neural networks. Consequently, past work has either resorted to
pre-training with maximum-likelihood or used convolutional networks for
generation. In this work, we show that recurrent neural networks can be trained
to generate text with GANs from scratch using curriculum learning, by slowly
teaching the model to generate sequences of increasing and variable length. We
empirically show that our approach vastly improves the quality of generated
sequences compared to a convolutional baseline.
Autism Classification Using Brain Functional Connectivity Dynamics and Machine Learning
Authors: Ravi Tejwani, Adam Liska, Hongyuan You, Jenna Reinen, Payel Das
Date: 2017-12-21 00:00:00
Tasks: BIG-bench Machine Learning, Functional Connectivity, General Classification

The goal of the present study is to identify autism using machine learning
techniques and resting-state brain imaging data, leveraging the temporal
variability of the functional connections (FC) as the only information. We
estimated and compared the FC variability across brain regions between typical,
healthy subjects and autistic population by analyzing brain imaging data from a
world-wide multi-site database known as ABIDE (Autism Brain Imaging Data
Exchange). Our analysis revealed that patients diagnosed with autism spectrum
disorder (ASD) show increased FC variability in several brain regions that are
associated with low FC variability in the typical brain. We then used the
enhanced FC variability of brain regions as features for training machine
learning models for ASD classification and achieved 65% accuracy in
identification of ASD versus control subjects within the dataset. We also used
node strength estimated from number of functional connections per node averaged
over the whole scan as features for ASD classification.The results reveal that
the dynamic FC measures outperform or are comparable with the static FC
measures in predicting ASD.
Siamese Neural Networks for One-shot detection of Railway Track Switches
Authors: Dattaraj J Rao, Shruti Mittal, S. Ritika
Date: 2017-12-21 00:00:00

Deep Learning methods have been extensively used to analyze video data to
extract valuable information by classifying image frames and detecting objects.
We describe a unique approach for using video feed from a moving Locomotive to
continuously monitor the Railway Track and detect significant assets like
Switches on the Track. The technique used here is called Siamese Networks,
which uses 2 identical networks to learn the similarity between of 2 images.
Here we will use a Siamese network to continuously compare Track images and
detect any significant difference in the Track. Switch will be one of those
images that will be different and we will find a mapping that clearly
distinguishes the Switch from other possible Track anomalies. The same method
will then be extended to detect any abnormalities on the Railway Track. Railway
Transportation is unique in the sense that is has wheeled vehicles, Trains
pulled by Locomotives, running on guided Rails at very high speeds nearing 200
mph. Multiple Tracks on the Rail network are connected to each other using an
equipment called Switch or a Turnout. Switch is either operated manually or
automatically through command from a Control center and it governs the movement
of Trains on different Tracks of the network. Accurate location of these
Switches is very important for the railroad and getting a true picture of their
state in field is important. Modern trains use high definition video cameras
facing the Track that continuously record video from track. Using a Siamese
network and comparing to benchmark images, we describe a method to monitor the
Track and highlight anomalies.
Neural Network Based Nonlinear Weighted Finite Automata
Authors: Tianyu Li, Guillaume Rabusseau, Doina Precup
Date: 2017-09-13 00:00:00

Weighted finite automata (WFA) can expressively model functions defined over
strings but are inherently linear models. Given the recent successes of
nonlinear models in machine learning, it is natural to wonder whether
ex-tending WFA to the nonlinear setting would be beneficial. In this paper, we
propose a novel model of neural network based nonlinearWFA model (NL-WFA) along
with a learning algorithm. Our learning algorithm is inspired by the spectral
learning algorithm for WFAand relies on a nonlinear decomposition of the
so-called Hankel matrix, by means of an auto-encoder network. The expressive
power of NL-WFA and the proposed learning algorithm are assessed on both
synthetic and real-world data, showing that NL-WFA can lead to smaller model
sizes and infer complex grammatical structures from data.