Publications

A Survey of Exploration Methods in Reinforcement Learning
Susan Amin
Maziar Gomrokchi
Harsh Satija
Herke van Hoof
Doina Precup
2021-09-01
ArXiv (preprint)
arxiv.org
arxiv.org
Deep Reinforcement Learning at the Edge of the Statistical Precipice
Rishabh Agarwal
Max Schwarzer
Pablo Samuel Castro
Aaron Courville
Marc Gendron-Bellemare
Deep reinforcement learning (RL) algorithms are predominantly evaluated by comparing their relative performance on a large suite of tasks. M… (see more)ost published results on deep RL benchmarks compare point estimates of aggregate performance such as mean and median scores across tasks, ignoring the statistical uncertainty implied by the use of a finite number of training runs. Beginning with the Arcade Learning Environment (ALE), the shift towards computationally-demanding benchmarks has led to the practice of evaluating only a small number of runs per task, exacerbating the statistical uncertainty in point estimates. In this paper, we argue that reliable evaluation in the few run deep RL regime cannot ignore the uncertainty in results without running the risk of slowing down progress in the field. We illustrate this point using a case study on the Atari 100k benchmark, where we find substantial discrepancies between conclusions drawn from point estimates alone versus a more thorough statistical analysis. With the aim of increasing the field's confidence in reported results with a handful of runs, we advocate for reporting interval estimates of aggregate performance and propose performance profiles to account for the variability in results, as well as present more robust and efficient aggregate metrics, such as interquartile mean scores, to achieve small uncertainty in results. Using such statistical tools, we scrutinize performance evaluations of existing algorithms on other widely used RL benchmarks including the ALE, Procgen, and the DeepMind Control Suite, again revealing discrepancies in prior comparisons. Our findings call for a change in how we evaluate performance in deep RL, for which we present a more rigorous evaluation methodology, accompanied with an open-source library rliable, to prevent unreliable results from stagnating the field.
2021-08-30
ArXiv (preprint)
arxiv.org
arxiv.org
Problems in the deployment of machine-learned models in health care
Joseph Paul Cohen
Tianshi Cao
Joseph D Viviano
Chinwei Huang
Michael Fralick
Marzyeh Ghassemi
M. Mamdani
R. Greiner
Yoshua Bengio
2021-08-30
Canadian Medical Association Journal (published)
doi.org
A Data Mining Analysis of Cross-Regional Study of Apparel Consumption
Osmud Rahman
Benjamin Fung
2021-08-28
Comprehensible Science (published)
doi.org
Successive-Cancellation Decoding of Reed-Muller Codes With Fast Hadamard Transform
Nghia Doan
Seyyed Ali Hashemi
Warren Gross
A novel permuted fast successive-cancellation list decoding algorithm with fast Hadamard transform (FHT-FSCL) is presented. The proposed dec… (see more)oder initializes
2021-08-28
ArXiv (preprint)
doi.org
arxiv.org
Approximate Bayesian Optimisation for Neural Networks
Nadhir Hassen
Irina Rish
2021-08-27
ArXiv (preprint)
arxiv.org
arxiv.org
Extracting Weighted Automata for Approximate Minimization in Language Modelling
Clara Lacroce
Prakash Panangaden
Guillaume Rabusseau
2021-08-25
Proceedings of the Fifteenth International Conference on Grammatical Inference (published)
proceedings.mlr.press
arxiv.org
HAD-Net: A Hierarchical Adversarial Knowledge Distillation Network for Improved Enhanced Tumour Segmentation Without Post-Contrast Images
Saverio Vadacchino
Raghav Mehta
Nazanin Mohammadi Sepahvand
Brennan Nichyporuk
James J. Clark
Tal Arbel
Segmentation of enhancing tumours or lesions from MRI is important for detecting new disease activity in many clinical contexts. However, ac… (see more)curate segmentation requires the inclusion of medical images (e.g., T1 post-contrast MRI) acquired after injecting patients with a contrast agent (e.g., Gadolinium), a process no longer thought to be safe. Although a number of modality-agnostic segmentation networks have been developed over the past few years, they have been met with limited success in the context of enhancing pathology segmentation. In this work, we present HAD-Net, a novel offline adversarial knowledge distillation (KD) technique, whereby a pre-trained teacher segmentation network, with access to all MRI sequences, teaches a student network, via hierarchical adversarial training, to better overcome the large domain shift presented when crucial images are absent during inference. In particular, we apply HAD-Net to the challenging task of enhancing tumour segmentation when access to post-contrast imaging is not available. The proposed network is trained and tested on the BraTS 2019 brain tumour segmentation challenge dataset, where it achieves performance improvements in the ranges of 16% - 26% over (a) recent modality-agnostic segmentation methods (U-HeMIS, U-HVED), (b) KD-Net adapted to this problem, (c) the pre-trained student network and (d) a non-hierarchical version of the network (AD-Net), in terms of Dice scores for enhancing tumour (ET). The network also shows improvements in tumour core (TC) Dice scores. Finally, the network outperforms both the baseline student network and AD-Net in terms of uncertainty quantification for enhancing tumour segmentation based on the BraTS 2019 uncertainty challenge metrics. Our code is publicly available at: https://github.com/SaverioVad/HAD_Net
2021-08-25
Proceedings of the Fourth Conference on Medical Imaging with Deep Learning (published)
proceedings.mlr.press
openreview.net
Monitoring non-pharmaceutical public health interventions during the COVID-19 pandemic
Yannan Shen
Guido Powell
Iris Ganser
Qulu Zheng
Chris Grundy
Anya Okhmatovskaia
David Buckeridge
2021-08-24
Scientific Data (published)
doi.org
Generating community measures of food purchasing activities using store-level electronic grocery transaction records: an ecological study in Montreal, Canada
Hiroshi Mamiya
Alexandra M. Schmidt
Erica E.M. Moodie
Yu Ma
David Buckeridge
2021-08-23
Public Health Nutrition (published)
doi.org
Deep Learning for Detecting Extreme Weather Patterns
Mayur Mudigonda
Mayur Mudigonda, Prabhat Ram
Prabhat Ram
Karthik Kashinath
Evan Racah
Ankur Mahesh
Yunjie Liu
Christopher Beckham
Jim Biard
Thorsten Kurth
Sookyung Kim
Samira Ebrahimi Kahou
Tegan Maharaj
Burlen Loring
Chris Pal
Travis O'Brien
K. Kunkel
Kenneth E. Kunkel
M. Wehner
Michael F. Wehner … (see 2 more)
W. Collins
William D. Collins
2021-08-20
Deep Learning for the Earth Sciences (published)
doi.org
Magnetoencephalography resting-state correlates of executive and language components of verbal fluency
Victor Oswald
Younes Zerouali
Aubrée Boulet-Craig
M. Krajinovic
Caroline Laverdière
D. Sinnett
Pierre W. Jolicoeur
Sarah Lippé
Karim Jerbi
Philippe Robaey
2021-08-20
Scientific Reports (published)
doi.org