Martin Klissarov
I am a PhD student supervised by Prof. Doina Precup at McGill University and Mila. I am particularly interested in scalable human-inspired approaches to intelligence.
Humans are able to learn new skills and adapt to changes in a sample efficient way, can reinforcement learning agents do the same?
My research is currently centered on meta reinforcement learning, together with temporal abstractions and off-policy methods, as a way to answer this question.
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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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Diffusion-Based Approximate Value Functions
Martin Klissarov, Doina Precup
International Conference on Machine Learning (ICML) Efficient Credit Assignemnt Workshop, 2018, Oral
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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
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- TMLR: 2022
- NeurIPS: 2020 / 2021 (Outstanding reviewer) / 2022 (Top reviewer)
- ICML: 2021 / 2022
- AAAI: 2021 / 2022
- ICLR: 2022 (Highlighted reviewer)
- CoLLAs: 2022
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- 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)
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