Publications

Emergent Communication under Competition
Michael Noukhovitch
Travis LaCroix
Angeliki Lazaridou
Aaron Courville
2021-01-01
AAMAS (publié)
doi.org
arxiv.org
An End-to-End Framework for Molecular Conformation Generation via Bilevel Programming
Minkai Xu
Wujie Wang
Shitong Luo
Chence Shi
Yoshua Bengio
Rafael G'omez-bombarelli
Jian Tang
Predicting molecular conformations (or 3D structures) from molecular graphs is a fundamental problem in many applications. Most existing app… (voir plus)roaches are usually divided into two steps by first predicting the distances between atoms and then generating a 3D structure through optimizing a distance geometry problem. However, the distances predicted with such two-stage approaches may not be able to consistently preserve the geometry of local atomic neighborhoods, making the generated structures unsatisfying. In this paper, we propose an end-to-end solution for molecular conformation prediction called ConfVAE based on the conditional variational autoencoder framework. Specifically, the molecular graph is first encoded in a latent space, and then the 3D structures are generated by solving a principled bilevel optimization program. Extensive experiments on several benchmark data sets prove the effectiveness of our proposed approach over existing state-of-the-art approaches. Code is available at https://github.com/MinkaiXu/ConfVAE-ICML21.
2021-01-01
ICML (publié)
proceedings.mlr.press
arxiv.org
Enjeux juridiques propres au modèle émergent des patients accompagnateurs dans les milieux de soins au Québec (Legal Issues Arising from the Emerging Model of Accompanying Patients in the Quebec Healthcare System)
Léa Boutrouille
Catherine Régis
Marie-Pascale Pomey
2021-01-01
SSRN Electronic Journal (publié)
doi.org
Episodes Meta Sequence S 2 Fast Update Slow Update Fast Update Slow Update
Kanika Madan
Nan Rosemary Ke
Anirudh Goyal
Bernhard Schölkopf
Yoshua Bengio
Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning age… (voir plus)nt interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic manner to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, metaparameters.We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.
Estimating the Impact of an Improvement to a Revenue Management System: An Airline Application
Greta Laage
Emma Frejinger
William L. Hamilton
Andrea Lodi
Guillaume Rabusseau
Airlines have been making use of highly complex Revenue Management Systems to maximize revenue for decades. Estimating the impact of changin… (voir plus)g one component of those systems on an important outcome such as revenue is crucial, yet very challenging. It is indeed the difference between the generated value and the value that would have been generated keeping business as usual, which is not observable. We provide a comprehensive overview of counterfactual prediction models and use them in an extensive computational study based on data from Air Canada to estimate such impact. We focus on predicting the counterfactual revenue and compare it to the observed revenue subject to the impact. Our microeconomic application and small expected treatment impact stand out from the usual synthetic control applications. We present accurate linear and deep-learning counterfactual prediction models which achieve respectively 1.1% and 1% of error and allow to estimate a simulated effect quite accurately.
2021-01-01
arXiv.org (prépublication)
dblp.uni-trier.de
Explaining by Analogy: Case-based Abductive Natural Language Inference
Ruben Cartuyvels
Graham Spinks
Marie Francine
Peter Clark
Isaac Cowhey
Oren Etzioni
Tushar Khot
Rajarshi Das
Ameya Godbole
Shehzaad Dhuliawala
Manzil Zaheer
Andrew McCallum
Dung Ngoc Thai
Ameya
Ethan Godbole
Jay-Yoon Perez
Lee
Lizhen
Ramón López De Mántaras
David Mcsherry … (voir 37 de plus)
David Bridge
Barry Leake
Susan Smyth
Craw.
Boi
Maryalice Faltings
Michael T Maher
Ken-552 Cox
Dorottya Demszky
Kelvin Guu
Percy Liang
Jacob Devlin
Ming-Wei Chang
Kenton Lee
Daniel Fried
Peter Jansen
Gus Hahn-Powell
Higher-575
Rebecca Emilie Sharp
M. Surdeanu
Zhengnan Xie
Sebastian Thiem
Jaycie Ryrholm Martin
Eliz-721 abeth Wainwright
Steven Marmorstein
Wenhan Xiong
Xiang Lorraine Li
Srini Iyer
Jingfei Du
Vikas Yadav
Steven Bethard
Zhilin Yang
Peng Qi
Saizheng Zhang
Yoshua Bengio
William W Cohen
Russ Salakhutdinov
Existing accounts of explanation emphasise 001 the role of prior experience and analogy in 002 the solution of new problems. However, most 0… (voir plus)03 of the contemporary models for multi-hop tex-004 tual inference construct explanations consider-005 ing each test case in isolation. This paradigm 006 is known to suffer from semantic drift, which 007 causes the construction of spurious explana-008 tions leading to wrong predictions. In con-009 trast, we propose an abductive framework for 010 multi-hop inference that adopts the retrieve - 011 reuse - revise paradigm largely studied in case-012 based reasoning . Specifically, we present 013 ETNA ( E xplana t io n by A nalogy), a novel 014 model that addresses unseen inference prob-015 lems by retrieving and adapting prior expla-016 nations from similar training examples. We 017 empirically evaluate the case-based abductive 018 framework on downstream commonsense and 019 scientific reasoning tasks. Our experiments 020 demonstrate that ETNA can be effectively in-021 tegrated with sparse and dense encoding mech-022 anisms or downstream transformers, achiev-023 ing strong performance when compared to ex-024 isting explainable approaches. Moreover, we 025 study the impact of the retrieve - reuse - revise 026 paradigm on explainability and semantic drift, 027 showing that it boosts the quality of the con-028 structed explanations, resulting in improved 029 downstream inference performance. 030
Exploring the Wasserstein metric for time-to-event analysis.
Tristan Sylvain
Margaux Luck
Joseph Paul Cohen
Heloise Cardinal
Andrea Lodi
Yoshua Bengio
2021-01-01
SPACA (publié)
proceedings.mlr.press
Exploring the Wasserstein metric for survival analysis
Tristan Sylvain
Margaux Luck
Joseph Paul Cohen
Andrea Lodi
Yoshua Bengio
Survival analysis is a type of semi-supervised task where the target output (the survival time) is often right-censored. Utilizing this info… (voir plus)rmation is a challenge because it is not obvious how to correctly incorporate these censored examples into a model. We study how three categories of loss functions can take advantage of this information: partial likelihood methods, rank methods, and our own classification method based on a Wasserstein metric (WM) and the non-parametric Kaplan Meier (KM) estimate of the probability density to impute the labels of censored examples. The proposed method predicts the probability distribution of an event, letting us compute survival curves and expected times of survival that are easier to interpret than the rank. We also demonstrate that this approach directly optimizes the expected C-index which is the most common evaluation metric for survival models.
Factorizing Declarative and Procedural Knowledge in Structured, Dynamical Environments
Anirudh Goyal
Alex Lamb
Phanideep Gampa
Philippe Beaudoin
Charles Blundell
Sergey Levine
Yoshua Bengio
Michael Curtis Mozer
2021-01-01
ICLR (publié)
openreview.net
openreview.net
Fast and Slow Learning of Recurrent Independent Mechanisms
Kanika Madan
Nan Rosemary Ke
Anirudh Goyal
Bernhard Schölkopf
Yoshua Bengio
Decomposing knowledge into interchangeable pieces promises a generalization advantage when there are changes in distribution. A learning age… (voir plus)nt interacting with its environment is likely to be faced with situations requiring novel combinations of existing pieces of knowledge. We hypothesize that such a decomposition of knowledge is particularly relevant for being able to generalize in a systematic manner to out-of-distribution changes. To study these ideas, we propose a particular training framework in which we assume that the pieces of knowledge an agent needs and its reward function are stationary and can be re-used across tasks. An attention mechanism dynamically selects which modules can be adapted to the current task, and the parameters of the selected modules are allowed to change quickly as the learner is confronted with variations in what it experiences, while the parameters of the attention mechanisms act as stable, slowly changing, meta-parameters. We focus on pieces of knowledge captured by an ensemble of modules sparsely communicating with each other via a bottleneck of attention. We find that meta-learning the modular aspects of the proposed system greatly helps in achieving faster adaptation in a reinforcement learning setup involving navigation in a partially observed grid world with image-level input. We also find that reversing the role of parameters and meta-parameters does not work nearly as well, suggesting a particular role for fast adaptation of the dynamically selected modules.
2021-01-01
ICLR (publié)
openreview.net
openreview.net
Faults in deep reinforcement learning programs: a taxonomy and a detection approach
Amin Nikanjam
Mohammad Mehdi Morovati
Foutse Khomh
Houssem Ben Braiek
2021-01-01
ArXiv (preprint)
doi.org
arxiv.org
Finite time analysis of temporal difference learning with linear function approximation: the tail averaged case
Gandharv Patil
Prashanth L.A.
Doina Precup
In this paper, we study the finite-time behaviour of temporal difference (TD) learning algorithms when combined with tail-averaging, and pr… (voir plus)esent instance dependent bounds on the parameter error of the tail-averaged TD iterate. Our error bounds hold in expectation as well as with high probability, exhibit a sharper rate of decay for the initial error (bias), and are comparable with existing bounds in the literature.