pwc-archive/papers-with-abstracts huggingface dataset:
Analysis of spectral clustering algorithms for community detection: the general bipartite setting
Authors: Zhixin Zhou, Arash A. Amini
Date: 2018-03-12 00:00:00
Tasks: Clustering, Community Detection, Stochastic Block Model

We consider spectral clustering algorithms for community detection under a
general bipartite stochastic block model (SBM). A modern spectral clustering
algorithm consists of three steps: (1) regularization of an appropriate
adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a
k-means type algorithm in the reduced spectral domain. We focus on the
adjacency-based spectral clustering and for the first step, propose a new
data-driven regularization that can restore the concentration of the adjacency
matrix even for the sparse networks. This result is based on recent work on
regularization of random binary matrices, but avoids using unknown population
level parameters, and instead estimates the necessary quantities from the data.
We also propose and study a novel variation of the spectral truncation step and
show how this variation changes the nature of the misclassification rate in a
general SBM. We then show how the consistency results can be extended to models
beyond SBMs, such as inhomogeneous random graph models with approximate
clusters, including a graphon clustering problem, as well as general
sub-Gaussian biclustering. A theme of the paper is providing a better
understanding of the analysis of spectral methods for community detection and
establishing consistency results, under fairly general clustering models and
for a wide regime of degree growths, including sparse cases where the average
expected degree grows arbitrarily slowly.
Machine Theory of Mind
Authors: Neil C. Rabinowitz, Frank Perbet, H. Francis Song, Chiyuan Zhang, S. M. Ali Eslami, Matthew Botvinick
Date: 2018-02-21 00:00:00
Proceeding: ICML 2018 7
Tasks: Deep Reinforcement Learning, Meta-Learning, Reinforcement Learning

Theory of mind (ToM; Premack & Woodruff, 1978) broadly refers to humans'
ability to represent the mental states of others, including their desires,
beliefs, and intentions. We propose to train a machine to build such models
too. We design a Theory of Mind neural network -- a ToMnet -- which uses
meta-learning to build models of the agents it encounters, from observations of
their behaviour alone. Through this process, it acquires a strong prior model
for agents' behaviour, as well as the ability to bootstrap to richer
predictions about agents' characteristics and mental states using only a small
number of behavioural observations. We apply the ToMnet to agents behaving in
simple gridworld environments, showing that it learns to model random,
algorithmic, and deep reinforcement learning agents from varied populations,
and that it passes classic ToM tasks such as the "Sally-Anne" test (Wimmer &
Perner, 1983; Baron-Cohen et al., 1985) of recognising that others can hold
false beliefs about the world. We argue that this system -- which autonomously
learns how to model other agents in its world -- is an important step forward
for developing multi-agent AI systems, for building intermediating technology
for machine-human interaction, and for advancing the progress on interpretable
AI.
Deep Reinforcement Learning for Join Order Enumeration
Authors: Ryan Marcus, Olga Papaemmanouil
Date: 2018-02-28 00:00:00
Tasks: Decision Making, Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Join order selection plays a significant role in query performance. However,
modern query optimizers typically employ static join enumeration algorithms
that do not receive any feedback about the quality of the resulting plan.
Hence, optimizers often repeatedly choose the same bad plan, as they do not
have a mechanism for "learning from their mistakes". In this paper, we argue
that existing deep reinforcement learning techniques can be applied to address
this challenge. These techniques, powered by artificial neural networks, can
automatically improve decision making by incorporating feedback from their
successes and failures. Towards this goal, we present ReJOIN, a
proof-of-concept join enumerator, and present preliminary results indicating
that ReJOIN can match or outperform the PostgreSQL optimizer in terms of plan
quality and join enumeration efficiency.
Event-based Moving Object Detection and Tracking
Authors: Anton Mitrokhin, Cornelia Fermuller, Chethan Parameshwara, Yiannis Aloimonos
Date: 2018-03-12 00:00:00
Tasks: 3D geometry, Event-based vision, Motion Detection, Moving Object Detection, Object, object-detection, Object Detection, Object Tracking, Optical Flow Estimation

Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are ideally suited for real-time motion analysis. The unique properties encompassed in the readings of such sensors provide high temporal resolution, superior sensitivity to light and low latency. These properties provide the grounds to estimate motion extremely reliably in the most sophisticated scenarios but they come at a price - modern event-based vision sensors have extremely low resolution and produce a lot of noise. Moreover, the asynchronous nature of the event stream calls for novel algorithms. This paper presents a new, efficient approach to object tracking with asynchronous cameras. We present a novel event stream representation which enables us to utilize information about the dynamic (temporal) component of the event stream, and not only the spatial component, at every moment of time. This is done by approximating the 3D geometry of the event stream with a parametric model; as a result, the algorithm is capable of producing the motion-compensated event stream (effectively approximating egomotion), and without using any form of external sensors in extremely low-light and noisy conditions without any form of feature tracking or explicit optical flow computation. We demonstrate our framework on the task of independent motion detection and tracking, where we use the temporal model inconsistencies to locate differently moving objects in challenging situations of very fast motion.
Geometrical Insights for Implicit Generative Modeling
Authors: Leon Bottou, Martin Arjovsky, David Lopez-Paz, Maxime Oquab
Date: 2017-12-21 00:00:00

Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differences. In particular, we can establish surprising approximate global convergence guarantees for the $1$-Wasserstein distance,even when the parametric generator has a nonconvex parametrization.
Policy Optimization by Genetic Distillation
Authors: Tanmay Gangwani, Jian Peng
Date: 2017-11-03 00:00:00
Proceeding: ICLR 2018 1
Tasks: Deep Reinforcement Learning, Imitation Learning, MuJoCo, Policy Gradient Methods, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Genetic algorithms have been widely used in many practical optimization
problems. Inspired by natural selection, operators, including mutation,
crossover and selection, provide effective heuristics for search and black-box
optimization. However, they have not been shown useful for deep reinforcement
learning, possibly due to the catastrophic consequence of parameter crossovers
of neural networks. Here, we present Genetic Policy Optimization (GPO), a new
genetic algorithm for sample-efficient deep policy optimization. GPO uses
imitation learning for policy crossover in the state space and applies policy
gradient methods for mutation. Our experiments on MuJoCo tasks show that GPO as
a genetic algorithm is able to provide superior performance over the
state-of-the-art policy gradient methods and achieves comparable or higher
sample efficiency.
Sorting by Swaps with Noisy Comparisons
Authors: Tomáš Gavenčiak, Barbara Geissmann, Johannes Lengler
Date: 2018-03-12 00:00:00

We study sorting of permutations by random swaps if each comparison gives the
wrong result with some fixed probability $p<1/2$. We use this process as
prototype for the behaviour of randomized, comparison-based optimization
heuristics in the presence of noisy comparisons. As quality measure, we compute
the expected fitness of the stationary distribution. To measure the runtime, we
compute the minimal number of steps after which the average fitness
approximates the expected fitness of the stationary distribution.
  We study the process where in each round a random pair of elements at
distance at most $r$ are compared. We give theoretical results for the extreme
cases $r=1$ and $r=n$, and experimental results for the intermediate cases. We
find a trade-off between faster convergence (for large $r$) and better quality
of the solution after convergence (for small $r$).
Gradient Augmented Information Retrieval with Autoencoders and Semantic Hashing
Authors: Sean Billings
Date: 2018-03-12 00:00:00
Tasks: Information Retrieval, Retrieval

This paper will explore the use of autoencoders for semantic hashing in the
context of Information Retrieval. This paper will summarize how to efficiently
train an autoencoder in order to create meaningful and low-dimensional
encodings of data. This paper will demonstrate how computing and storing the
closest encodings to an input query can help speed up search time and improve
the quality of our search results. The novel contributions of this paper
involve using the representation of the data learned by an auto-encoder in
order to augment our search query in various ways. I present and evaluate the
new gradient search augmentation (GSA) approach, as well as the more well-known
pseudo-relevance-feedback (PRF) adjustment. I find that GSA helps to improve
the performance of the TF-IDF based information retrieval system, and PRF
combined with GSA works best overall for the systems compared in this paper.
Probabilistic and Regularized Graph Convolutional Networks
Authors: Sean Billings
Date: 2018-03-12 00:00:00

This paper explores the recently proposed Graph Convolutional Network
architecture proposed in (Kipf & Welling, 2016) The key points of their work is
summarized and their results are reproduced. Graph regularization and
alternative graph convolution approaches are explored. I find that explicit
graph regularization was correctly rejected by (Kipf & Welling, 2016). I
attempt to improve the performance of GCN by approximating a k-step transition
matrix in place of the normalized graph laplacian, but I fail to find positive
results. Nonetheless, the performance of several configurations of this GCN
variation is shown for the Cora, Citeseer, and Pubmed datasets.
Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts
Authors: Faisal Alshargi, Saeedeh Shekarpour, Tommaso Soru, Amit Sheth
Date: 2018-03-12 00:00:00

Although there is an emerging trend towards generating embeddings for primarily unstructured data and, recently, for structured data, no systematic suite for measuring the quality of embeddings has been proposed yet. This deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of the encoded structure as well as semantic patterns in the embedding space. In this paper, we introduce a framework containing three distinct tasks concerned with the individual aspects of ontological concepts: (i) the categorization aspect, (ii) the hierarchical aspect, and (iii) the relational aspect. Then, in the scope of each task, a number of intrinsic metrics are proposed for evaluating the quality of the embeddings. Furthermore, w.r.t. this framework, multiple experimental studies were run to compare the quality of the available embedding models. Employing this framework in future research can reduce misjudgment and provide greater insight about quality comparisons of embeddings for ontological concepts. We positioned our sampled data and code at https://github.com/alshargi/Concept2vec under GNU General Public License v3.0.
Machine Learning Harnesses Molecular Dynamics to Discover New $μ$ Opioid Chemotypes
Authors: Evan N. Feinberg, Amir Barati Farimani, Rajendra Uprety, Amanda Hunkele, Gavril W. Pasternak, Susruta Majumdar, Vijay S. Pande
Date: 2018-03-12 00:00:00
Tasks: BIG-bench Machine Learning

Computational chemists typically assay drug candidates by virtually screening
compounds against crystal structures of a protein despite the fact that some
targets, like the $\mu$ Opioid Receptor and other members of the GPCR family,
traverse many non-crystallographic states. We discover new conformational
states of $\mu OR$ with molecular dynamics simulation and then machine learn
ligand-structure relationships to predict opioid ligand function. These
artificial intelligence models identified a novel $\mu$ opioid chemotype.
Correction by Projection: Denoising Images with Generative Adversarial Networks
Authors: Subarna Tripathi, Zachary C. Lipton, Truong Q. Nguyen
Date: 2018-03-12 00:00:00
Tasks: Denoising

Generative adversarial networks (GANs) transform low-dimensional latent
vectors into visually plausible images. If the real dataset contains only clean
images, then ostensibly, the manifold learned by the GAN should contain only
clean images. In this paper, we propose to denoise corrupted images by finding
the nearest point on the GAN manifold, recovering latent vectors by minimizing
distances in image space. We first demonstrate that given a corrupted version
of an image that truly lies on the GAN manifold, we can approximately recover
the latent vector and denoise the image, obtaining significantly higher
quality, comparing with BM3D. Next, we demonstrate that latent vectors
recovered from noisy images exhibit a consistent bias. By subtracting this bias
before projecting back to image space, we improve denoising results even
further. Finally, even for unseen images, our method performs better at
denoising better than BM3D. Notably, the basic version of our method (without
bias correction) requires no prior knowledge on the noise variance. To achieve
the highest possible denoising quality, the best performing signal processing
based methods, such as BM3D, require an estimate of the blur kernel.
Accuracy-Reliability Cost Function for Empirical Variance Estimation
Authors: Enrico Camporeale
Date: 2018-03-12 00:00:00
Tasks: Philosophy

In this paper we focus on the problem of assigning uncertainties to
single-point predictions. We introduce a cost function that encodes the
trade-off between accuracy and reliability in probabilistic forecast. We derive
analytic formula for the case of forecasts of continuous scalar variables
expressed in terms of Gaussian distributions. The Accuracy-Reliability cost
function can be used to empirically estimate the variance in heteroskedastic
regression problems (input dependent noise), by solving a two-objective
optimization problem. The simple philosophy behind this strategy is that
predictions based on the estimated variances should be both accurate and
reliable (i.e. statistical consistent with observations). We show several
examples with synthetic data, where the underlying hidden noise function can be
accurately recovered, both in one and multi-dimensional problems. The practical
implementation of the method has been done using a Neural Network and, in the
one-dimensional case, with a simple polynomial fit.
Predicting Crime Using Spatial Features
Authors: Fateha Khanam Bappee, Amilcar Soares Junior, Stan Matwin
Date: 2018-03-12 00:00:00
Tasks: Clustering, Crime Prediction, Position

Our study aims to build a machine learning model for crime prediction using
geospatial features for different categories of crime. The reverse geocoding
technique is applied to retrieve open street map (OSM) spatial data. This study
also proposes finding hotpoints extracted from crime hotspots area found by
Hierarchical Density-Based Spatial Clustering of Applications with Noise
(HDBSCAN). A spatial distance feature is then computed based on the position of
different hotpoints for various types of crime and this value is used as a
feature for classifiers. We test the engineered features in crime data from
Royal Canadian Mounted Police of Halifax, NS. We observed a significant
performance improvement in crime prediction using the new generated spatial
features.
An Introduction to Image Synthesis with Generative Adversarial Nets
Authors: He Huang, Philip S. Yu, Changhu Wang
Date: 2018-03-12 00:00:00
Tasks: Image Generation, Image-to-Image Translation, Translation

There has been a drastic growth of research in Generative Adversarial Nets
(GANs) in the past few years. Proposed in 2014, GAN has been applied to various
applications such as computer vision and natural language processing, and
achieves impressive performance. Among the many applications of GAN, image
synthesis is the most well-studied one, and research in this area has already
demonstrated the great potential of using GAN in image synthesis. In this
paper, we provide a taxonomy of methods used in image synthesis, review
different models for text-to-image synthesis and image-to-image translation,
and discuss some evaluation metrics as well as possible future research
directions in image synthesis with GAN.
Finding Influential Training Samples for Gradient Boosted Decision Trees
Authors: Boris Sharchilev, Yury Ustinovsky, Pavel Serdyukov, Maarten de Rijke
Date: 2018-02-19 00:00:00
Proceeding: ICML 2018 7
Tasks: Computational Efficiency

We address the problem of finding influential training samples for a
particular case of tree ensemble-based models, e.g., Random Forest (RF) or
Gradient Boosted Decision Trees (GBDT). A natural way of formalizing this
problem is studying how the model's predictions change upon leave-one-out
retraining, leaving out each individual training sample. Recent work has shown
that, for parametric models, this analysis can be conducted in a
computationally efficient way. We propose several ways of extending this
framework to non-parametric GBDT ensembles under the assumption that tree
structures remain fixed. Furthermore, we introduce a general scheme of
obtaining further approximations to our method that balance the trade-off
between performance and computational complexity. We evaluate our approaches on
various experimental setups and use-case scenarios and demonstrate both the
quality of our approach to finding influential training samples in comparison
to the baselines and its computational efficiency.
PotentialNet for Molecular Property Prediction
Authors: Evan N. Feinberg, Debnil Sur, Zhenqin Wu, Brooke E. Husic, Huanghao Mai, Yang Li, Saisai Sun, Jianyi Yang, Bharath Ramsundar, Vijay S. Pande
Date: 2018-03-12 00:00:00
Tasks: ARC, BIG-bench Machine Learning, Clustering, Drug Discovery, Feature Engineering, Molecular Property Prediction, Prediction, Property Prediction

The arc of drug discovery entails a multiparameter optimization problem
spanning vast length scales. They key parameters range from solubility
(angstroms) to protein-ligand binding (nanometers) to in vivo toxicity
(meters). Through feature learning---instead of feature engineering---deep
neural networks promise to outperform both traditional physics-based and
knowledge-based machine learning models for predicting molecular properties
pertinent to drug discovery. To this end, we present the PotentialNet family of
graph convolutions. These models are specifically designed for and achieve
state-of-the-art performance for protein-ligand binding affinity. We further
validate these deep neural networks by setting new standards of performance in
several ligand-based tasks. In parallel, we introduce a new metric, the
Regression Enrichment Factor $EF_\chi^{(R)}$, to measure the early enrichment
of computational models for chemical data. Finally, we introduce a
cross-validation strategy based on structural homology clustering that can more
accurately measure model generalizability, which crucially distinguishes the
aims of machine learning for drug discovery from standard machine learning
tasks.
False Discovery Rate Control via Debiased Lasso
Authors: Adel Javanmard, Hamid Javadi
Date: 2018-03-12 00:00:00
Tasks: Variable Selection

We consider the problem of variable selection in high-dimensional statistical
models where the goal is to report a set of variables, out of many predictors
$X_1, \dotsc, X_p$, that are relevant to a response of interest. For linear
high-dimensional model, where the number of parameters exceeds the number of
samples $(p>n)$, we propose a procedure for variables selection and prove that
it controls the "directional" false discovery rate (FDR) below a pre-assigned
significance level $q\in [0,1]$. We further analyze the statistical power of
our framework and show that for designs with subgaussian rows and a common
precision matrix $\Omega\in\mathbb{R}^{p\times p}$, if the minimum nonzero
parameter $\theta_{\min}$ satisfies $$\sqrt{n} \theta_{\min} - \sigma
\sqrt{2(\max_{i\in [p]}\Omega_{ii})\log\left(\frac{2p}{qs_0}\right)} \to
\infty\,,$$ then this procedure achieves asymptotic power one. Our framework is
built upon the debiasing approach and assumes the standard condition $s_0 =
o(\sqrt{n}/(\log p)^2)$, where $s_0$ indicates the number of true positives
among the $p$ features. Notably, this framework achieves exact directional FDR
control without any assumption on the amplitude of unknown regression
parameters, and does not require any knowledge of the distribution of
covariates or the noise level. We test our method in synthetic and real data
experiments to assess its performance and to corroborate our theoretical
results.
Dissimilarity-based representation for radiomics applications
Authors: Hongliu Cao, Simon Bernard, Laurent Heutte, Robert Sabourin
Date: 2018-03-12 00:00:00
Tasks: Diagnostic, feature selection, MULTI-VIEW LEARNING

Radiomics is a term which refers to the analysis of the large amount of
quantitative tumor features extracted from medical images to find useful
predictive, diagnostic or prognostic information. Many recent studies have
proved that radiomics can offer a lot of useful information that physicians
cannot extract from the medical images and can be associated with other
information like gene or protein data. However, most of the classification
studies in radiomics report the use of feature selection methods without
identifying the machine learning challenges behind radiomics. In this paper, we
first show that the radiomics problem should be viewed as an high dimensional,
low sample size, multi view learning problem, then we compare different
solutions proposed in multi view learning for classifying radiomics data. Our
experiments, conducted on several real world multi view datasets, show that the
intermediate integration methods work significantly better than filter and
embedded feature selection methods commonly used in radiomics.