Martin Klissarov

I am a PhD student supervised by Prof. Doina Precup and Prof. Marlos C. Machado at McGill University and Mila. I am particularly interested in scalable human-inspired approaches to intelligence.

Humans are able to continually adapt in diverse and creative ways, can AI agents do the same?

My research is currently centered on the discovery and construction of temporal abstractions [skills / tools / programs] to answer this question. In particular, I'm interested in building abstractions from the ground-up by learning representations from interaction, as well as from the top-down by distilling abstract knowledge contained in large models (e.g. LLMs).

Recently, I've been increasingly invested in the importance of building agents whose behaviour we can interpret and align.

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News
Research
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Motif: Intrinsic Motivation from Artificial Intelligence Feedback
Martin Klissarov*, Pierluca D'Oro*, Shagun Sodhani, Roberta Raileanu, Pierre-Luc Bacon, Pascal Vincent, Amy Zhang and Mikael Henaff
International Conference on Learning Representations (ICRL), 2024
FMDM Workshop at NeurIPS 2023, Spotlight
ALOE Workshop at NeurIPS 2023, Spotlight

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Deep Laplacian-Based Options for Temporally-Extended Exploration
Martin Klissarov and Marlos C. Machado
International Conference on Machine Learning (ICML), 2023

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Adaptive Interest for Emphatic Reinforcement Learning
Martin Klissarov, Rasool Fakoor, Jonas Mueller, Kavosh Asadi, Taesup Kim and Alex Smola
Neural Information Processing Systems (NeurIPS), 2022
DARL workshop at ICML 2022, Spotlight

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Flexible Option Learning
Martin Klissarov and Doina Precup
Neural Information Processing Systems (NeurIPS), 2021, Spotlight

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Reward Propagation using Graph Convolutional Networks
Martin Klissarov and Doina Precup
Neural Information Processing Systems (NeurIPS), 2020, Spotlight

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Options of Interest: Temporal Abstraction with Interest Functions
Khimya Khetarpal, Martin Klissarov, Maxime Chevalier-Boisvert, Pierre-Luc Bacon, Doina Precup
Association for the Advancement of Artificial Intelligence (AAAI), 2020

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Variational State Encoding

Martin Klissarov*, Riashat Islam*, Khimya Khetarpal, Doina Precup
The Multi-disciplinary Conference on Reinforcement Learning and Decision Making (RLDM), 2019

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When Waiting is not an Option: Learning Options using the Deliberation Cost

Jean Harb, Pierre-Luc Bacon, Martin Klissarov, Doina Precup
Association for the Advancement of Artificial Intelligence (AAAI), 2018

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Learning Options End-to-End for Continuous Action Tasks

Martin Klissarov, Pierre-Luc Bacon, Jean Harb, Doina Precup
Neural Information Processing Systems (NIPS) Hierarchichal Reinforcement Learning Workshop, 2017

Education
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McGill University2020 - Present
PhD in Computer Science
Supervisor: Prof. Doina Precup and Prof. Marlos C. Machado

mcgill

McGill University2018 - 2020
MSc in Computer Science
Supervisor: Prof. Doina Precup

Reviewing
  • TMLR: 2022 / 2023
  • NeurIPS: 2020 / 2021 (Outstanding reviewer) / 2022 (Top reviewer) / 2023
  • ICML: 2021 / 2022 / 2023
  • AAAI: 2021 / 2022
  • ICLR: 2022 (Highlighted reviewer) / 2023
  • CoLLAs: 2022
Organisation Comittee
  • CoLLAs: 2022
Selected Awards
  • Natural Sciences and Engineering Research Council of Canada (NSERC) Alexander Graham Bell Canada Graduate Scholarship (2020-2023)
  • Fonds de Recherche du Québec - Nature et Technologie (FRQNT) Masters Research Scholarship (2018-2020)
  • McGill Graduate Excellence Award (2018-2020)
  • Ordre des Ingénieurs Foundation Excellence Scholarship (2016)

A special thanks to this template.