Doina Precup

Mohammad Sami Nur Islam Islam

Jesse Farebrother

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Doctorat - McGill

Superviseur⋅e principal⋅e :

Eilif Benjamin Muller

Doctorat - McGill

Doctorat - McGill

Maîtrise recherche - McGill

Arushi Jain

Doctorat - McGill

Doctorat - McGill

Postdoctorat - McGill

Elaine Lau

Maîtrise recherche - McGill

Jonathan Lebensold

Collaborateur·rice alumni - McGill

Baccalauréat - McGill

Ray Luo

Doctorat - McGill

Superviseur⋅e principal⋅e :

G McCracken

Doctorat - McGill

Nazanin Mohammadi Sepahvand

Doctorat - McGill

Shahrad Mohammadzadeh

Maîtrise recherche - McGill

Superviseur⋅e principal⋅e :

Gabriela Moisescu-Pareja

Collaborateur·rice de recherche - McGill

Co-superviseur⋅e :

Irina Rish

Padideh Nouri

Doctorat - UdeM

Co-superviseur⋅e :

Charles Onu

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Nate Rahn

Doctorat - McGill

Superviseur⋅e principal⋅e :

Marc Gendron-Bellemare

Sahand Rezaei-Shoshtari

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Co-superviseur⋅e :

Blake Richards

samiemandana@gmail.com

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Nishanth Anand Vemgal

Doctorat - McGill

Doctorat - McGill

Doctorat - McGill

Co-superviseur⋅e :

Samira Ebrahimi Kahou

Site web

Zihan Wang

Doctorat - McGill

Site web

Guangyuan Wang

Stagiaire de recherche - McGill

Steve Wen

Maîtrise recherche - McGill

Co-superviseur⋅e :

Gregory Dudek

Skipper : combiner l’abstraction spatiale et temporelle afin d’améliorer la généralisation

Zijing Wu

Doctorat - McGill

Co-superviseur⋅e :

Doctorat - McGill

Harry Zhao

Collaborateur·rice alumni - McGill

Co-superviseur⋅e :

Billets de blogue

Generic thumbnail for Mila Blog articles.

22 février 2024

par

Mingde Harry Zhao

Safa Alver

Harm van Seijen

Romain Laroche

Doina Precup

Yoshua Bengio

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Publications

Flexible Option Learning

Martin Klissarov

Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation

Emmanuel Bengio

Moksh J. Jain

Maksym Korablyov

Yoshua Bengio

This paper is about the problem of learning a stochastic policy for generating an object (like a molecular graph) from a sequence of actions… (voir plus), such that the probability of generating an object is proportional to a given positive reward for that object. Whereas standard return maximization tends to converge to a single return-maximizing sequence, there are cases where we would like to sample a diverse set of high-return solutions. These arise, for example, in black-box function optimization when few rounds are possible, each with large batches of queries, where the batches should be diverse, e.g., in the design of new molecules. One can also see this as a problem of approximately converting an energy function to a generative distribution. While MCMC methods can achieve that, they are expensive and generally only perform local exploration. Instead, training a generative policy amortizes the cost of search during training and yields to fast generation. Using insights from Temporal Difference learning, we propose GFlowNet, based on a view of the generative process as a flow network, making it possible to handle the tricky case where different trajectories can yield the same final state, e.g., there are many ways to sequentially add atoms to generate some molecular graph. We cast the set of trajectories as a flow and convert the flow consistency equations into a learning objective, akin to the casting of the Bellman equations into Temporal Difference methods. We prove that any global minimum of the proposed objectives yields a policy which samples from the desired distribution, and demonstrate the improved performance and diversity of GFlowNet on a simple domain where there are many modes to the reward function, and on a molecule synthesis task.

openreview.net

Improving Long-Term Metrics in Recommendation Systems using Short-Horizon Offline RL

Bogdan Mazoure

Paul Mineiro

Pavithra Srinath

Reza Sharifi Sedeh

Adith Swaminathan

We study session-based recommendation scenarios where we want to recommend items to users during sequential interactions to improve their lo… (voir plus)ng-term utility. Optimizing a long-term metric is challenging because the learning signal (whether the recommendations achieved their desired goals) is delayed and confounded by other user interactions with the system. Immediately measurable proxies such as clicks can lead to suboptimal recommendations due to misalignment with the long-term metric. Many works have applied episodic reinforcement learning (RL) techniques for session-based recommendation but these methods do not account for policy-induced drift in user intent across sessions. We develop a new batch RL algorithm called Short Horizon Policy Improvement (SHPI) that approximates policy-induced distribution shifts across sessions. By varying the horizon hyper-parameter in SHPI, we recover well-known policy improvement schemes in the RL literature. Empirical results on four recommendation tasks show that SHPI can outperform matrix factorization, offline bandits, and offline RL baselines. We also provide a stable and computationally efficient implementation using weighted regression oracles.

2021-01-01

arXiv.org (prépublication)

dblp.uni-trier.de

Preferential Temporal Difference Learning

Nishanth Anand

2021-01-01

ICML (publié)

arxiv.org

Randomized Exploration in Reinforcement Learning with General Value Function Approximation

Haque Ishfaq

Qiwen Cui

Viet Bang Nguyen

Alex Ayoub

Zhuoran Yang

Zhaoran Wang

Lin Yang

2021-01-01

International Conference on Machine Learning (publié)

Randomized Least Squares Policy Optimization

Haque Ishfaq

Zhuoran Yang

Andrei-Stefan Lupu

Viet Bang Nguyen

Lewis Liu

Riashat Islam

Zhaoran Wang

Policy Optimization (PO) methods with function approximation are one of the most popular classes of Reinforcement Learning (RL) algorithms. … (voir plus)However, designing provably efﬁcient policy optimization algorithms remains a challenge. Recent work in this area has focused on incorporating upper conﬁdence bound (UCB)-style bonuses to drive exploration in policy optimization. In this paper, we present Randomized Least Squares Policy Optimization (RLSPO) which is inspired by Thompson Sampling. We prove that, in an episodic linear kernel MDP setting, RLSPO achieves (cid:101) O ( d 3 / 2 H 3 / 2 √ T ) worst-case (frequentist) regret, where H is the number of episodes, T is the total number of steps and d is the feature dimension. Finally, we evaluate RLSPO empirically and show that it is competitive with existing provably efﬁcient PO algorithms.

Temporally Abstract Partial Models

Khimya Khetarpal

Zafarali Ahmed

Gheorghe Comanici

Humans and animals have the ability to reason and make predictions about different courses of action at many time scales. In reinforcement l… (voir plus)earning, option models (Sutton, Precup \& Singh, 1999; Precup, 2000) provide the framework for this kind of temporally abstract prediction and reasoning. Natural intelligent agents are also able to focus their attention on courses of action that are relevant or feasible in a given situation, sometimes termed affordable actions. In this paper, we define a notion of affordances for options, and develop temporally abstract partial option models, that take into account the fact that an option might be affordable only in certain situations. We analyze the trade-offs between estimation and approximation error in planning and learning when using such models, and identify some interesting special cases. Additionally, we empirically demonstrate the ability to learn both affordances and partial option models online resulting in improved sample efficiency and planning time in the Taxi domain.

openreview.net

On the Expressivity of Markov Reward

David Abel

Will Dabney

Anna Harutyunyan

Mark K. Ho

Michael L. Littman

Satinder Singh

Reward is the driving force for reinforcement-learning agents. This paper is dedicated to understanding the expressivity of reward as a way … (voir plus)to capture tasks that we would want an agent to perform. We frame this study around three new abstract notions of"task"that might be desirable: (1) a set of acceptable behaviors, (2) a partial ordering over behaviors, or (3) a partial ordering over trajectories. Our main results prove that while reward can express many of these tasks, there exist instances of each task type that no Markov reward function can capture. We then provide a set of polynomial-time algorithms that construct a Markov reward function that allows an agent to optimize tasks of each of these three types, and correctly determine when no such reward function exists. We conclude with an empirical study that corroborates and illustrates our theoretical findings.

openreview.net

Where Did You Learn That From? Surprising Effectiveness of Membership Inference Attacks Against Temporally Correlated Data in Deep Reinforcement Learning

Maziar Gomrokchi

Susan Amin

Hossein Aboutalebi

Alexander Wong

2021-01-01

arXiv.org (prépublication)

dblp.uni-trier.de

Phylogenetic Manifold Regularization: A semi-supervised approach to predict transcription factor binding sites

Faizy Ahsan

Alexandre Drouin

Franccois Laviolette

Mathieu Blanchette

The computational prediction of transcription factor binding sites remains a challenging problems in bioinformatics, despite significant met… (voir plus)hodological developments from the field of machine learning. Such computational models are essential to help interpret the non-coding portion of human genomes, and to learn more about the regulatory mechanisms controlling gene expression. In parallel, massive genome sequencing efforts have produced assembled genomes for hundred of vertebrate species, but this data is underused. We present PhyloReg, a new semi-supervised learning approach that can be used for a wide variety of sequence-to-function prediction problems, and that takes advantage of hundreds of millions of years of evolution to regularize predictors and improve accuracy. We demonstrate that PhyloReg can be used to better train a previously proposed deep learning model of transcription factor binding. Simulation studies further help delineate the benefits of the a pproach. G ains in prediction accuracy are obtained over a broad set of transcription factors and cell types.

2020-12-16

2020 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (publié)

doi.org

Interference and Generalization in Temporal Difference Learning

Emmanuel Bengio

Joelle Pineau

We study the link between generalization and interference in temporal-difference (TD) learning. Interference is defined as the inner product… (voir plus) of two different gradients, representing their alignment. This quantity emerges as being of interest from a variety of observations about neural networks, parameter sharing and the dynamics of learning. We find that TD easily leads to low-interference, under-generalizing parameters, while the effect seems reversed in supervised learning. We hypothesize that the cause can be traced back to the interplay between the dynamics of interference and bootstrapping. This is supported empirically by several observations: the negative relationship between the generalization gap and interference in TD, the negative effect of bootstrapping on interference and the local coherence of targets, and the contrast between the propagation rate of information in TD(0) versus TD(

2020-11-21

Proceedings of the 37th International Conference on Machine Learning (publié)

arxiv.org

Invariant Causal Prediction for Block MDPs

Amy Zhang

Clare Lyle

Shagun Sodhani

Angelos Filos

Marta Z. Kwiatkowska

Joelle Pineau

Yarin Gal

Generalization across environments is critical to the successful application of reinforcement learning algorithms to real-world challenges. … (voir plus)In this paper, we consider the problem of learning abstractions that generalize in block MDPs, families of environments with a shared latent state space and dynamics structure over that latent space, but varying observations. We leverage tools from causal inference to propose a method of invariant prediction to learn model-irrelevance state abstractions (MISA) that generalize to novel observations in the multi-environment setting. We prove that for certain classes of environments, this approach outputs with high probability a state abstraction corresponding to the causal feature set with respect to the return. We further provide more general bounds on model error and generalization error in the multi-environment setting, in the process showing a connection between causal variable selection and the state abstraction framework for MDPs. We give empirical evidence that our methods work in both linear and nonlinear settings, attaining improved generalization over single- and multi-task baselines.

2020-11-21

Proceedings of the 37th International Conference on Machine Learning (publié)