Publications

Overview of the TREC 2019 Fair Ranking Track
Asia J. Biega
Fernando Diaz
Michael D. Ekstrand
Sebastian Kohlmeier
The goal of the TREC Fair Ranking track was to develop a benchmark for evaluating retrieval systems in terms of fairness to different conten… (voir plus)t providers in addition to classic notions of relevance. As part of the benchmark, we defined standardized fairness metrics with evaluation protocols and released a dataset for the fair ranking problem. The 2019 task focused on reranking academic paper abstracts given a query. The objective was to fairly represent relevant authors from several groups that were unknown at the system submission time. Thus, the track emphasized the development of systems which have robust performance across a variety of group definitions. Participants were provided with querylog data (queries, documents, and relevance) from Semantic Scholar. This paper presents an overview of the track, including the task definition, descriptions of the data and the annotation process, as well as a comparison of the performance of submitted systems.
2020-03-25
ArXiv (preprint)
arxiv.org
arxiv.org
Multinational Investigation of Fracture Risk with Antidepressant Use by Class, Drug, and Indication
Robyn Tamblyn
David W. Bates
David Buckeridge
William G. Dixon
Nadyne Girard
Jennifer S. Haas
Bettina Habib
Usman Iqbal
Jack Li
Therese Sheppard
Antidepressants increase the risk of falls and fracture in older adults. However, risk estimates vary considerably even in comparable popula… (voir plus)tions, limiting the usefulness of current evidence for clinical decision making. Our aim was to apply a common protocol to cohorts of older antidepressant users in multiple jurisdictions to estimate fracture risk associated with different antidepressant classes, drugs, doses, and potential treatment indications.
2020-03-17
Journal of the American Geriatrics Society (publié)
doi.org
Online Fast Adaptation and Knowledge Accumulation: a New Approach to Continual Learning
Massimo Caccia
Pau Rodriguez
Oleksiy Ostapenko
Fabrice Normandin
Min Lin
Lucas Caccia
Issam Hadj Laradji
Irina Rish
Alexande Lacoste
David Vazquez
Laurent Charlin
2020-03-12
ArXiv (prépublication)
arxiv.org
arxiv.org
Improving Convolutional Neural Networks Via Conservative Field Regularisation and Integration
Dominique Beaini
Sofiane Wozniak Achiche
Maxime Raison
2020-03-11
ArXiv (prépublication)
arxiv.org
arxiv.org
Tensorized Random Projections
Beheshteh T. Rakhshan
Guillaume Rabusseau
We introduce a novel random projection technique for efficiently reducing the dimension of very high-dimensional tensors. Building upon clas… (voir plus)sical results on Gaussian random projections and Johnson-Lindenstrauss transforms~(JLT), we propose two tensorized random projection maps relying on the tensor train~(TT) and CP decomposition format, respectively. The two maps offer very low memory requirements and can be applied efficiently when the inputs are low rank tensors given in the CP or TT format. Our theoretical analysis shows that the dense Gaussian matrix in JLT can be replaced by a low-rank tensor implicitly represented in compressed form with random factors, while still approximately preserving the Euclidean distance of the projected inputs. In addition, our results reveal that the TT format is substantially superior to CP in terms of the size of the random projection needed to achieve the same distortion ratio. Experiments on synthetic data validate our theoretical analysis and demonstrate the superiority of the TT decomposition.
2020-03-11
ArXiv (preprint)
arxiv.org
arxiv.org
IG-RL: Inductive Graph Reinforcement Learning for Massive-Scale Traffic Signal Control
François-Xavier Devailly
Denis Larocque
Laurent Charlin
Scaling adaptive traffic signal control involves dealing with combinatorial state and action spaces. Multi-agent reinforcement learning atte… (voir plus)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)
doi.org
arxiv.org
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 … (voir 39 de plus)
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 (publié)
doi.org
RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning
Stefano Alletto
Shenyang Huang
Vincent Francois-Lavet
Yohei Nakata
Guillaume Rabusseau
Almost all neural architecture search methods are evaluated in terms of performance (i.e. test accuracy) of the model structures that it fin… (voir plus)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 (prépublication)
arxiv.org
arxiv.org
Tensor Networks for Language Modeling
Jacob Miller
Guillaume Rabusseau
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… (voir plus) 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 (prépublication)
arxiv.org
arxiv.org
Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller
Guillaume Rabusseau
John Anthony Terilla
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (voir plus)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 (publié)
proceedings.mlr.press
Tensor Networks for Probabilistic Sequence Modeling
Jacob Miller
Guillaume Rabusseau
John Anthony Terilla
Tensor networks are a powerful modeling framework developed for computational many-body physics, which have only recently been applied withi… (voir plus)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
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 (publié)
doi.org