Publications

Continuous Domain Adaptation with Variational Domain-Agnostic Feature Replay

Qicheng Lao

Xiang Jiang

Mohammad Havaei

Yoshua Bengio

Learning in non-stationary environments is one of the biggest challenges in machine learning. Non-stationarity can be caused by either task … (see more)drift, i.e., the drift in the conditional distribution of labels given the input data, or the domain drift, i.e., the drift in the marginal distribution of the input data. This paper aims to tackle this challenge in the context of continuous domain adaptation, where the model is required to learn new tasks adapted to new domains in a non-stationary environment while maintaining previously learned knowledge. To deal with both drifts, we propose variational domain-agnostic feature replay, an approach that is composed of three components: an inference module that filters the input data into domain-agnostic representations, a generative module that facilitates knowledge transfer, and a solver module that applies the filtered and transferable knowledge to solve the queries. We address the two fundamental scenarios in continuous domain adaptation, demonstrating the effectiveness of our proposed approach for practical usage.

2020-03-09

ArXiv (preprint)

IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control

FranÃ§ois-Xavier Devailly

Denis Larocque

Laurent Charlin

Scaling adaptive traffic signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning atte… (see more)mpts to address this challenge by distributing control to specialized agents. However, specialization hinders generalization and transferability, and the computational graphs underlying neural-network architectures—dominating in the multi-agent setting—do not offer the flexibility to handle an arbitrary number of entities which changes both between road networks, and over time as vehicles traverse the network. We introduce Inductive Graph Reinforcement Learning (IG-RL) based on graph-convolutional networks which adapts to the structure of any road network, to learn detailed representations of traffic signal controllers and their surroundings. Our decentralized approach enables learning of a transferable-adaptive-traffic-signal-control policy. After being trained on an arbitrary set of road networks, our model can generalize to new road networks and traffic distributions, with no additional training and a constant number of parameters, enabling greater scalability compared to prior methods. Furthermore, our approach can exploit the granularity of available data by capturing the (dynamic) demand at both the lane level and the vehicle level. The proposed method is tested on both road networks and traffic settings never experienced during training. We compare IG-RL to multi-agent reinforcement learning and domain-specific baselines. In both synthetic road networks and in a larger experiment involving the control of the 3,971 traffic signals of Manhattan, we show that different instantiations of IG-RL outperform baselines.

2020-03-06

ArXiv (preprint)

Dissecting the phenotypic heterogeneity in sensory features in autism spectrum disorder: a factor mixture modelling approach

Julian Tillmann

M. Uljarevic

Daisy Crawley

G. Dumas

Eva Loth

D. Murphy

J. Buitelaar

Tony Charman

Jumana Sara Bonnie Sarah Christian Thomas Carsten Michael Daniel Claudia Yvette Bhismadev Ineke Flavio Dell’ Guillaume Christine Jessica Vincent Pilar David Hannah Joerg Mark H. Emily J. H. Prantik Meng-Chuan Xavier Liogier Michael David J. René Luke Andreas Carolin Nico Laurence Marianne Bob Gahan Antonio M. Barbara Amber Jessica Roberto Roberto Heike Jack Steve C. R. Caroline Marcel P. Ahmad

Jumana Sara Bonnie Sarah Christian Thomas Carsten Michael Ahmad Ambrosino Auyeung Baumeister Beckmann Bourge

Jumana Ahmad

Sara Ambrosino

Bonnie Auyeung

Sarah Baumeister

Christian Beckmann

Thomas Bourgeron

Carsten Bours

Michael Brammer

Daniel Brandeis

Claudia Brogna … (see 39 more)

Yvette de Bruijn

Bhismadev Chakrabarti

Ineke Cornelissen

Flavio Dell’ Acqua

Guillaume Dumas

Christine Ecker

Jessica Faulkner

Vincent Frouin

Pilar Garcés

David Goyard

Hannah Hayward

Joerg F. Hipp

Mark Johnson

Emily J. H. Jones

Prantik Kundu

Meng-Chuan Lai

Xavier Liogier D’ardhuy

Michael V. Lombardo

David J. Lythgoe

René Mandl

Luke Mason

Andreas Meyer-Lindenberg

Carolin Moessnang

Nico Mueller

Laurence O’Dwyer

Marianne Oldehinkel

Bob Oranje

Gahan Pandina

Antonio Persico

Barbara Ruggeri

Amber N. V. Ruigrok

Jessica Sabet

Roberto Sacco

Roberto Toro

Heike Tost

Jack Waldman

Steve C. R. Williams

Caroline Wooldridge

Marcel P. Zwiers

2020-03-05

Molecular Autism (published)

Not one model ﬁts all: unfairness in RSFC-based prediction of behavioral data in African American

Jingwei Li

Danilo Bzdok

Avram J. Holmes

Thomas B.t. Yeo

Sarah Genon

14 Helmholtz AI kick-off meeting 5 Mar 2020, 14:17:33 Page 1/1 Abstract #14 | Poster Not one model fits all: unfairness in RSFC-based predic… (see more)tion of behavioral data in African American J. Li , D. Bzdok, A. Holmes, T. Yeo, S. Genon 1 Forschungszentrum Julich, Institute of Neuroscience and Medicine, Jülich, Germany 2 McGill University, Department of Biomedical Imaging, Montreal, Canada 3 National University of Singapore, ECE, CSC, CIRC, N.1 & MNP, Singapore, Singapore 4 Yale University, New Haven, United States of America While predictive models are expected to play a major role in personalized medicine approaches in the future, biases towards specific population groups have been evidenced, hence raising concerns about the risks of unfairness of machine learning algorithms. As great hopes and intense work have been invested recently in the prediction of behavioral phenotypes based on brain resting-state functional connectivity (RSFC), we here examined potential differences in RSFC-based predictive models of behavioral data between African American (AA) and White American (WA) samples matched for the main demographic, anthropometric, behavioral and in-scanner motion variables. We used resting-fMRI data with 58 behavioral measures of 953 subjects comprising 130 African American (AA) and 724 White American (WA). For each subject, a 419 x 419 matrix summarizing connectivity of 419 brain regions was computed. Matching between AA and WA was performed at the subject level by creating 102 pairs of AA and WA subjects, matched for 6 types of variables (age, sex, intracranial volume, education, in-scanner motion and behavioral scores). We performed 10-fold nested cross-validation by randomly splitting the 102 pairs across 10 sets. The remaining 749 subjects were also divided across the 10 sets. A predictive model was built for each behavioral variable by using kernel ridge regression. All analyses focused on the 102 matched AA and WA groups. After FDR correction (q 0.05), no significant difference was found between the matched AA and WA groups for the matching variables. Out of 58 behavioral variables, 38 showed significantly above chance prediction accuracies (based on permutation test, FDR corrected). Overall, average prediction performance for these variables was higher in the WA group than in the AA group. Furthermore, significant differences in prediction performance between the two groups were found in 35 behavioral variables (FDR corrected; q 0.05). Our results suggest that RSFC-based prediction models of behavioral phenotype trained on the entire HCP population show different prediction performance in different subsets of the population. This suggest that one model might not fit all that, in some cases, RSFC-based predictive models might have poorer prediction accuracies for African Americans compared to matched White Americans. Future work should evaluate the factors contributing to these discrepancies and the potential consequences, as well as possible recommendations.

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning

Stefano Alletto

Shenyang Huang

Vincent Francois-Lavet

Yohei Nakata

Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it fin… (see more)ds. Should it be the only metric for a good autoML approach? To examine aspects beyond performance, we propose a set of criteria aimed at evaluating the core of autoML problem: the amount of human intervention required to deploy these methods into real world scenarios. Based on our proposed evaluation checklist, we study the effectiveness of a random search strategy for fully automated multimodal neural architecture search. Compared to traditional methods that rely on manually crafted feature extractors, our method selects each modality from a large search space with minimal human supervision. We show that our proposed random search strategy performs close to the state of the art on the AV-MNIST dataset while meeting the desirable characteristics for a fully automated design process.

2020-03-02

ArXiv (preprint)

Tensor Networks for Language Modeling

Jacob Miller

John Anthony Terilla

The tensor network formalism has enjoyed over two decades of success in modeling the behavior of complex quantum-mechanical systems, but has… (see more) only recently and sporadically been leveraged in machine learning. Here we introduce a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We identify several distinctive features of this recurrent generative model, notably the ability to condition or marginalize sampling on characters at arbitrary locations within a sequence, with no need for approximate sampling methods. Despite the sequential architecture of u-MPS, we show that a recursive evaluation algorithm can be used to parallelize its inference and training, with a string of length n only requiring parallel time

2020-03-02

ArXiv (preprint)

Tensor Networks for Probabilistic Sequence Modeling

Jacob Miller

John Anthony Terilla

Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (see more)n machine learning. In this work we utilize a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We first show that u-MPS enable sequence-level parallelism, with length-n sequences able to be evaluated in depth O(log n). We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions, each one defined by a regular expression. Special cases of this algorithm correspond to autoregressive and fill-in-the-blank sampling, but more complex regular expressions permit the generation of richly structured text in a manner that has no direct analogue in current generative models. Experiments on synthetic text data find u-MPS outperforming LSTM baselines in several sampling tasks, and demonstrate strong generalization in the presence of limited data.

2020-03-02

International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

Tensor Networks for Probabilistic Sequence Modeling

Jacob Miller

John Anthony Terilla

Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (see more)n machine learning. In this work we utilize a uniform matrix product state (u-MPS) model for probabilistic modeling of sequence data. We first show that u-MPS enable sequence-level parallelism, with length-n sequences able to be evaluated in depth O(log n). We then introduce a novel generative algorithm giving trained u-MPS the ability to efficiently sample from a wide variety of conditional distributions, each one defined by a regular expression. Special cases of this algorithm correspond to autoregressive and fill-in-the-blank sampling, but more complex regular expressions permit the generation of richly structured text in a manner that has no direct analogue in current generative models. Experiments on synthetic text data find u-MPS outperforming LSTM baselines in several sampling tasks, and demonstrate strong generalization in the presence of limited data.

2020-03-02

International Conference on Artificial Intelligence and Statistics (published)

dblp.uni-trier.de

Multiple Kernel Learning-Based Transfer Regression for Electric Load Forecasting

Di Wu

Boyu Wang

Doina Precup

Benoit Boulet

Electric load forecasting, especially short-term load forecasting (STLF), is becoming more and more important for power system operation. We… (see more) propose to use multiple kernel learning (MKL) for residential electric load forecasting which provides more flexibility than traditional kernel methods. Computation time is an important issue for short-term forecasting, especially for energy scheduling. However, conventional MKL methods usually lead to complicated optimization problems. Another practical issue for this application is that there may be a very limited amount of data available to train a reliable forecasting model for a new house, while at the same time we may have historical data collected from other houses which can be leveraged to improve the prediction performance for the new house. In this paper, we propose a boosting-based framework for MKL regression to deal with the aforementioned issues for STLF. In particular, we first adopt boosting to learn an ensemble of multiple kernel regressors and then extend this framework to the context of transfer learning. Furthermore, we consider two different settings: homogeneous transfer learning and heterogeneous transfer learning. Experimental results on residential data sets demonstrate that forecasting error can be reduced by a large margin with the knowledge learned from other houses.

2020-03-01

IEEE Transactions on Smart Grid (published)

Seven pillars of precision digital health and medicine

Arash Shaban-Nejad

Martin Michalowski

Niels Peek

John S. Brownstein

David Buckeridge

2020-03-01

Artificial Intelligence in Medicine (published)

On the Morality of Artificial Intelligence

Alexandra Luccioni

Yoshua Bengio

Examines ethical principles and guidelines that surround machine learning and artificial intelligence.

2020-03-01

IEEE Technology and Society Magazine (published)