pwc-archive/papers-with-abstracts huggingface dataset:
Parallel Markov Chain Monte Carlo for Bayesian Hierarchical Models with Big Data, in Two Stages
Authors: Zheng Wei, Erin M. Conlon
Date: 2017-12-16 00:00:00

Due to the escalating growth of big data sets in recent years, new Bayesian
Markov chain Monte Carlo (MCMC) parallel computing methods have been developed.
These methods partition large data sets by observations into subsets. However,
for Bayesian nested hierarchical models, typically only a few parameters are
common for the full data set, with most parameters being group-specific. Thus,
parallel Bayesian MCMC methods that take into account the structure of the
model and split the full data set by groups rather than by observations are a
more natural approach for analysis. Here, we adapt and extend a recently
introduced two-stage Bayesian hierarchical modeling approach, and we partition
complete data sets by groups. In stage 1, the group-specific parameters are
estimated independently in parallel. The stage 1 posteriors are used as
proposal distributions in stage 2, where the target distribution is the full
model. Using three-level and four-level models, we show in both simulation and
real data studies that results of our method agree closely with the full data
analysis, with greatly increased MCMC efficiency and greatly reduced
computation times. The advantages of our method versus existing parallel MCMC
computing methods are also described.
Learning formation energy of inorganic compounds using matrix variate deep Gaussian process
Authors: Saket Mishra, Piyush Tagade
Date: 2018-12-22 00:00:00
Tasks: Formation Energy

Future advancement of engineering applications is dependent on design of
novel materials with desired properties. Enormous size of known chemical space
necessitates use of automated high throughput screening to search the desired
material. The high throughput screening uses quantum chemistry calculations to
predict material properties, however, computational complexity of these
calculations often imposes prohibitively high cost on the search for desired
material. This critical bottleneck is resolved by using deep machine learning
to emulate the quantum computations. However, the deep learning algorithms
require a large training dataset to ensure an acceptable generalization, which
is often unavailable a-priory. In this paper, we propose a deep Gaussian
process based approach to develop an emulator for quantum calculations. We
further propose a novel molecular descriptor that enables implementation of the
proposed approach. As demonstrated in this paper, the proposed approach can be
implemented using a small dataset. We demonstrate efficacy of our approach for
prediction of formation energy of inorganic molecules.
Data-driven Air Quality Characterisation for Urban Environments: a Case Study
Authors: Yuchao Zhou, Suparna De, Gideon Ewa, Charith Perera, Klaus Moessner
Date: 2018-12-01 00:00:00
Tasks: BIG-bench Machine Learning, Missing Labels, Time Series, Time Series Analysis, Time Series Prediction

The economic and social impact of poor air quality in towns and cities is
increasingly being recognised, together with the need for effective ways of
creating awareness of real-time air quality levels and their impact on human
health. With local authority maintained monitoring stations being
geographically sparse and the resultant datasets also featuring missing labels,
computational data-driven mechanisms are needed to address the data sparsity
challenge. In this paper, we propose a machine learning-based method to
accurately predict the Air Quality Index (AQI), using environmental monitoring
data together with meteorological measurements. To do so, we develop an air
quality estimation framework that implements a neural network that is enhanced
with a novel Non-linear Autoregressive neural network with exogenous input
(NARX), especially designed for time series prediction. The framework is
applied to a case study featuring different monitoring sites in London, with
comparisons against other standard machine-learning based predictive algorithms
showing the feasibility and robust performance of the proposed method for
different kinds of areas within an urban region.
Avoiding a Tragedy of the Commons in the Peer Review Process
Authors: D. Sculley, Jasper Snoek, Alex Wiltschko
Date: 2018-12-18 00:00:00
Tasks: Position

Peer review is the foundation of scientific publication, and the task of
reviewing has long been seen as a cornerstone of professional service. However,
the massive growth in the field of machine learning has put this community
benefit under stress, threatening both the sustainability of an effective
review process and the overall progress of the field. In this position paper,
we argue that a tragedy of the commons outcome may be avoided by emphasizing
the professional aspects of this service. In particular, we propose a rubric to
hold reviewers to an objective standard for review quality. In turn, we also
propose that reviewers be given appropriate incentive. As one possible such
incentive, we explore the idea of financial compensation on a per-review basis.
We suggest reasonable funding models and thoughts on long term effects.
Micro- and Macro-Level Churn Analysis of Large-Scale Mobile Games
Authors: Xi Liu, Muhe Xie, Xidao Wen, Rui Chen, Yong Ge, Nick Duffield, Na Wang
Date: 2019-01-14 00:00:00
Tasks: Attribute

As mobile devices become more and more popular, mobile gaming has emerged as
a promising market with billion-dollar revenues. A variety of mobile game
platforms and services have been developed around the world. A critical
challenge for these platforms and services is to understand the churn behavior
in mobile games, which usually involves churn at micro level (between an app
and a specific user) and macro level (between an app and all its users).
Accurate micro-level churn prediction and macro-level churn ranking will
benefit many stakeholders such as game developers, advertisers, and platform
operators. In this paper, we present the first large-scale churn analysis for
mobile games that supports both micro-level churn prediction and macro-level
churn ranking. For micro-level churn prediction, in view of the common
limitations of the state-of-the-art methods built upon traditional machine
learning models, we devise a novel semi-supervised and inductive embedding
model that jointly learns the prediction function and the embedding function
for user-app relationships. We model these two functions by deep neural
networks with a unique edge embedding technique that is able to capture both
contextual information and relationship dynamics. We also design a novel
attributed random walk technique that takes into consideration both topological
adjacency and attribute similarities. To address macro-level churn ranking, we
propose to construct a relationship graph with estimated micro-level churn
probabilities as edge weights and adapt link analysis algorithms on the graph.
We devise a simple algorithm SimSum and adapt two more advanced algorithms
PageRank and HITS. The performance of our solutions for the two-level churn
analysis problems is evaluated on real-world data collected from the Samsung
Game Launcher platform.
Development of Mobile-Interfaced Machine Learning-Based Predictive Models for Improving Students Performance in Programming Courses
Authors: Temitayo Matthew Fagbola, Ibrahim Adepoju Adeyanju, Olatayo Olaniyan, Adebimpe Esan, Bolaji Omodunbi, Ayodele Oloyede, Funmilola Egbetola
Date: 2019-01-09 00:00:00
Tasks: BIG-bench Machine Learning, Decision Making

Student performance modelling (SPM) is a critical step to assessing and
improving students performances in their learning discourse. However, most
existing SPM are based on statistical approaches, which on one hand are based
on probability, depicting that results are based on estimation; and on the
other hand, actual influences of hidden factors that are peculiar to students,
lecturers, learning environment and the family, together with their overall
effect on student performance have not been exhaustively investigated. In this
paper, Student Performance Models (SPM) for improving students performance in
programming courses were developed using M5P Decision Tree (MDT) and Linear
Regression Classifier (LRC). The data used was gathered using a structured
questionnaire from 295 students in 200 and 300 levels of study who offered Web
programming, C or JAVA at Federal University, Oye-Ekiti, Nigeria between 2012
and 2016. Hidden factors that are significant to students performance in
programming were identified. The relevant data gathered, normalized, coded and
prepared as variable and factor datasets, and fed into the MDT algorithm and
LRC to develop the predictive models. The evaluation results obtained indicate
that the variable-based LRC produced the best model in terms of MAE, RMSE, RAE
and the RRSE having yielded the least values in all the evaluations conducted.
Further results obtained established the strong significance of attitude of
students and lecturers, fearful perception of students, erratic power supply,
university facilities, student health and students attendance to the
performance of students in programming courses. The variable-based LRC model
presented in this paper could provide baseline information about students
performance thereby offering better decision making towards improving
teaching/learning outcomes in programming courses.
Comparing two deep learning sequence-based models for protein-protein interaction prediction
Authors: Florian Richoux, Charlène Servantie, Cynthia Borès, Stéphane Téletchéa
Date: 2019-01-15 00:00:00
Tasks: Deep Learning

Biological data are extremely diverse, complex but also quite sparse. The
recent developments in deep learning methods are offering new possibilities for
the analysis of complex data. However, it is easy to be get a deep learning
model that seems to have good results but is in fact either overfitting the
training data or the validation data. In particular, the fact to overfit the
validation data, called "information leak", is almost never treated in papers
proposing deep learning models to predict protein-protein interactions (PPI).
In this work, we compare two carefully designed deep learning models and show
pitfalls to avoid while predicting PPIs through machine learning methods. Our
best model predicts accurately more than 78% of human PPI, in very strict
conditions both for training and testing. The methodology we propose here allow
us to have strong confidences about the ability of a model to scale up on
larger datasets. This would allow sharper models when larger datasets would be
available, rather than current models prone to information leaks. Our solid
methodological foundations shall be applicable to more organisms and whole
proteome networks predictions.
Detecting Behavioral Engagement of Students in the Wild Based on Contextual and Visual Data
Authors: Eda Okur, Nese Alyuz, Sinem Aslan, Utku Genc, Cagri Tanriover, Asli Arslan Esme
Date: 2019-01-15 00:00:00

To investigate the detection of students' behavioral engagement (On-Task vs.
Off-Task), we propose a two-phase approach in this study. In Phase 1,
contextual logs (URLs) are utilized to assess active usage of the content
platform. If there is active use, the appearance information is utilized in
Phase 2 to infer behavioral engagement. Incorporating the contextual
information improved the overall F1-scores from 0.77 to 0.82. Our
cross-classroom and cross-platform experiments showed the proposed generic and
multi-modal behavioral engagement models' applicability to a different set of
students or different subject areas.
Effectiveness Assessment of Cyber-Physical Systems
Authors: Gérald Rocher, Jean-Yves Tigli, Stéphane Lavirotte, Nhan Le Thanh
Date: 2019-01-10 00:00:00
Tasks: Autonomous Vehicles

By achieving their purposes through interactions with the physical world, Cyber-Physical Systems (CPS) pose new challenges in terms of dependability. Indeed, the evolution of the physical systems they control with transducers can be affected by surrounding physical processes over which they have no control and which may potentially hamper the achievement of their purposes. While it is illusory to hope for a comprehensive model of the physical environment at design time to anticipate and remove faults that may occur once these systems are deployed, it becomes necessary to evaluate their degree of effectiveness in vivo. In this paper, the degree of effectiveness is formally defined and generalized in the context of the measure theory. The measure is developed in the context of the Transferable Belief Model (TBM), an elaboration on the Dempster-Shafer Theory (DST) of evidence so as to handle epistemic and aleatory uncertainties respectively pertaining the users' expectations and the natural variability of the physical environment. The TBM is used in conjunction with the Input/Output Hidden Markov Modeling framework (we denote by Ev-IOHMM) to specify the expected evolution of the physical system controlled by the CPS and the tolerances towards uncertainties. The measure of effectiveness is then obtained from the forward algorithm, leveraging the conflict entailed by the successive combinations of the beliefs obtained from observations of the physical system and the beliefs corresponding to its expected evolution. The proposed approach is applied to autonomous vehicles and show how the degree of effectiveness can be used for bench-marking their controller relative to the highway code speed limitations and passengers' well-being constraints, both modeled through an Ev-IOHMM.
CONet: A Cognitive Ocean Network
Authors: Huimin Lu, Dong Wang, Yujie Li, Jianru Li, Xin Li, Hyoungseop Kim, Seiichi Serikawa, Iztok Humar
Date: 2019-01-09 00:00:00

The scientific and technological revolution of the Internet of Things has
begun in the area of oceanography. Historically, humans have observed the ocean
from an external viewpoint in order to study it. In recent years, however,
changes have occurred in the ocean, and laboratories have been built on the
seafloor. Approximately 70.8% of the Earth's surface is covered by oceans and
rivers. The Ocean of Things is expected to be important for disaster
prevention, ocean-resource exploration, and underwater environmental
monitoring. Unlike traditional wireless sensor networks, the Ocean Network has
its own unique features, such as low reliability and narrow bandwidth. These
features will be great challenges for the Ocean Network. Furthermore, the
integration of the Ocean Network with artificial intelligence has become a
topic of increasing interest for oceanology researchers. The Cognitive Ocean
Network (CONet) will become the mainstream of future ocean science and
engineering developments. In this article, we define the CONet. The
contributions of the paper are as follows: (1) a CONet architecture is proposed
and described in detail; (2) important and useful demonstration applications of
the CONet are proposed; and (3) future trends in CONet research are presented.
BUOCA: Budget-Optimized Crowd Worker Allocation
Authors: Mehrnoosh Sameki, Sha Lai, Kate K. Mays, Lei Guo, Prakash Ishwar, Margrit Betke
Date: 2019-01-11 00:00:00

Due to concerns about human error in crowdsourcing, it is standard practice
to collect labels for the same data point from multiple internet workers. We
here show that the resulting budget can be used more effectively with a
flexible worker assignment strategy that asks fewer workers to analyze
easy-to-label data and more workers to analyze data that requires extra
scrutiny. Our main contribution is to show how the allocations of the number of
workers to a task can be computed optimally based on task features alone,
without using worker profiles. Our target tasks are delineating cells in
microscopy images and analyzing the sentiment toward the 2016 U.S. presidential
candidates in tweets. We first propose an algorithm that computes
budget-optimized crowd worker allocation (BUOCA). We next train a machine
learning system (BUOCA-ML) that predicts an optimal number of crowd workers
needed to maximize the accuracy of the labeling. We show that the computed
allocation can yield large savings in the crowdsourcing budget (up to 49
percent points) while maintaining labeling accuracy. Finally, we envisage a
human-machine system for performing budget-optimized data analysis at a scale
beyond the feasibility of crowdsourcing.
Metadata Embeddings for User and Item Cold-start Recommendations
Authors: Maciej Kula
Date: 2015-07-30 00:00:00
Tasks: TAG

I present a hybrid matrix factorisation model representing users and items as
linear combinations of their content features' latent factors. The model
outperforms both collaborative and content-based models in cold-start or sparse
interaction data scenarios (using both user and item metadata), and performs at
least as well as a pure collaborative matrix factorisation model where
interaction data is abundant. Additionally, feature embeddings produced by the
model encode semantic information in a way reminiscent of word embedding
approaches, making them useful for a range of related tasks such as tag
recommendations.
Hybrid Task Cascade for Instance Segmentation
Authors: Kai Chen, Jiangmiao Pang, Jiaqi Wang, Yu Xiong, Xiaoxiao Li, Shuyang Sun, Wansen Feng, Ziwei Liu, Jianping Shi, Wanli Ouyang, Chen Change Loy, Dahua Lin
Date: 2019-01-22 00:00:00
Proceeding: CVPR 2019 6
Tasks: Instance Segmentation, object-detection, Object Detection, Open-Ended Question Answering, Segmentation, Semantic Segmentation

Cascade is a classic yet powerful architecture that has boosted performance
on various tasks. However, how to introduce cascade to instance segmentation
remains an open question. A simple combination of Cascade R-CNN and Mask R-CNN
only brings limited gain. In exploring a more effective approach, we find that
the key to a successful instance segmentation cascade is to fully leverage the
reciprocal relationship between detection and segmentation. In this work, we
propose a new framework, Hybrid Task Cascade (HTC), which differs in two
important aspects: (1) instead of performing cascaded refinement on these two
tasks separately, it interweaves them for a joint multi-stage processing; (2)
it adopts a fully convolutional branch to provide spatial context, which can
help distinguishing hard foreground from cluttered background. Overall, this
framework can learn more discriminative features progressively while
integrating complementary features together in each stage. Without bells and
whistles, a single HTC obtains 38.4 and 1.5 improvement over a strong Cascade
Mask R-CNN baseline on MSCOCO dataset. Moreover, our overall system achieves
48.6 mask AP on the test-challenge split, ranking 1st in the COCO 2018
Challenge Object Detection Task. Code is available at:
https://github.com/open-mmlab/mmdetection.
Robust Recovery Controller for a Quadrupedal Robot using Deep Reinforcement Learning
Authors: Joonho Lee, Jemin Hwangbo, Marco Hutter
Date: 2019-01-22 00:00:00
Tasks: Deep Reinforcement Learning, Navigate, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

The ability to recover from a fall is an essential feature for a legged robot
to navigate in challenging environments robustly. Until today, there has been
very little progress on this topic. Current solutions mostly build upon
(heuristically) predefined trajectories, resulting in unnatural behaviors and
requiring considerable effort in engineering system-specific components. In
this paper, we present an approach based on model-free Deep Reinforcement
Learning (RL) to control recovery maneuvers of quadrupedal robots using a
hierarchical behavior-based controller. The controller consists of four neural
network policies including three behaviors and one behavior selector to
coordinate them. Each of them is trained individually in simulation and
deployed directly on a real system. We experimentally validate our approach on
the quadrupedal robot ANYmal, which is a dog-sized quadrupedal system with 12
degrees of freedom. With our method, ANYmal manifests dynamic and reactive
recovery behaviors to recover from an arbitrary fall configuration within less
than 5 seconds. We tested the recovery maneuver more than 100 times, and the
success rate was higher than 97 %.
Understanding Multi-Step Deep Reinforcement Learning: A Systematic Study of the DQN Target
Authors: J. Fernando Hernandez-Garcia, Richard S. Sutton
Date: 2019-01-22 00:00:00
Tasks: Deep Reinforcement Learning, Q-Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Multi-step methods such as Retrace($\lambda$) and $n$-step $Q$-learning have
become a crucial component of modern deep reinforcement learning agents. These
methods are often evaluated as a part of bigger architectures and their
evaluations rarely include enough samples to draw statistically significant
conclusions about their performance. This type of methodology makes it
difficult to understand how particular algorithmic details of multi-step
methods influence learning. In this paper we combine the $n$-step action-value
algorithms Retrace, $Q$-learning, Tree Backup, Sarsa, and $Q(\sigma)$ with an
architecture analogous to DQN. We test the performance of all these algorithms
in the mountain car environment; this choice of environment allows for faster
training times and larger sample sizes. We present statistical analyses on the
effects of the off-policy correction, the backup length parameter $n$, and the
update frequency of the target network on the performance of these algorithms.
Our results show that (1) using off-policy correction can have an adverse
effect on the performance of Sarsa and $Q(\sigma)$; (2) increasing the backup
length $n$ consistently improved performance across all the different
algorithms; and (3) the performance of Sarsa and $Q$-learning was more robust
to the effect of the target network update frequency than the performance of
Tree Backup, $Q(\sigma)$, and Retrace in this particular task.
Regularized Weighted Chebyshev Approximations for Support Estimation
Authors: i, Chien, Olgica Milenkovic
Date: 2019-01-22 00:00:00

We introduce a new method for estimating the support size of an unknown distribution which provably matches the performance bounds of the state-of-the-art techniques in the area and outperforms them in practice. In particular, we present both theoretical and computer simulation results that illustrate the utility and performance improvements of our method. The theoretical analysis relies on introducing a new weighted Chebyshev polynomial approximation method, jointly optimizing the bias and variance components of the risk, and combining the weighted minmax polynomial approximation method with discretized semi-infinite programming solvers. Such a setting allows for casting the estimation problem as a linear program (LP) with a small number of variables and constraints that may be solved as efficiently as the original Chebyshev approximation problem. Our technique is tested on synthetic data and used to address an important problem in computational biology - estimating the number of bacterial genera in the human gut. On synthetic datasets, for practically relevant sample sizes, we observe significant improvements in the value of the worst-case risk compared to existing methods. For the bioinformatics application, using metagenomic data from the NIH Human Gut and the American Gut Microbiome Projects, we generate a list of frequencies of bacterial taxa that allows us to estimate the number of bacterial genera to approximately 2300.