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Priyesh Vijayan

Doctorat - McGill
Superviseur⋅e principal⋅e
Doina Precup
Co-supervisor
Samira Ebrahimi Kahou
Sujets de recherche
Apprentissage de représentations
Apprentissage par renforcement
Apprentissage spectral
Apprentissage sur graphes
Réseaux de neurones en graphes
Site web
Google Scholar
GitHub

Publications

Revisiting Laplacian Representations for Value Function Approximation in Deep RL
Priyesh Vijayan
Padideh Nouri
Rishav
Sarath Chandar
Yash Chandak
Mathieu Reymond
Samira Ebrahimi Kahou
Doina Precup
Proto-value functions (PVFs) introduced Laplacian embeddings as an effective feature basis for value-function approximation; however, their … (voir plus)utility remained limited to small, fully known state spaces. Recent work has scaled Laplacian embeddings to high-dimensional inputs, using them for reward shaping and option discovery in goal-directed tasks, yet only as auxiliary signals, rather than directly using them as features for value functions. In this paper, we learn Laplacian eigenvectors online and employ them as features for Q-learning in 23 Atari games. We empirically demonstrate that these online–learned embeddings substantially improve model-free RL in large, high-dimensional domains. We demonstrate that enriching state representations with action embeddings yields additional gains under both behavior-policy and uniform-random policies. Additionally, we introduce the Fusion architecture, which augments the representation with useful inductive bias at the embedding level. To assess the usefulness of each embedding used in the Fusion architecture, we use Shapley values analysis.
2025-06-22
rl-conference.cc/RLC/2025/Workshop/IBRL (publié)
openreview.net
openreview.net
Generative Active Learning for the Search of Small-molecule Protein Binders
Maksym Korablyov
Cheng-Hao Liu
Moksh J. Jain
Almer M. van der Sloot
Eric Jolicoeur
Edward Ruediger
Andrei Cristian Nica
Emmanuel Bengio
Kostiantyn Lapchevskyi
Daniel St-Cyr
Doris Alexandra Schuetz
Victor I Butoi
Jarrid Rector-Brooks
Simon R. Blackburn
Leo Feng
Hadi Nekoei
Sai Krishna Gottipati
Priyesh Vijayan
Prateek Gupta
Ladislav Rampášek … (voir 14 de plus)
Sasikanth Avancha
Pierre-Luc Bacon
William L. Hamilton
Brooks Paige
Sanchit Misra
Stanisław Jastrzębski
Bharat Kaul
Doina Precup
José Miguel Hernández-Lobato
Marwin Segler
Michael M. Bronstein
Anne Marinier
Mike Tyers
Yoshua Bengio
Despite substantial progress in machine learning for scientific discovery in recent years, truly de novo design of small molecules which exh… (voir plus)ibit a property of interest remains a significant challenge. We introduce LambdaZero, a generative active learning approach to search for synthesizable molecules. Powered by deep reinforcement learning, LambdaZero learns to search over the vast space of molecules to discover candidates with a desired property. We apply LambdaZero with molecular docking to design novel small molecules that inhibit the enzyme soluble Epoxide Hydrolase 2 (sEH), while enforcing constraints on synthesizability and drug-likeliness. LambdaZero provides an exponential speedup in terms of the number of calls to the expensive molecular docking oracle, and LambdaZero de novo designed molecules reach docking scores that would otherwise require the virtual screening of a hundred billion molecules. Importantly, LambdaZero discovers novel scaffolds of synthesizable, drug-like inhibitors for sEH. In in vitro experimental validation, a series of ligands from a generated quinazoline-based scaffold were synthesized, and the lead inhibitor N-(4,6-di(pyrrolidin-1-yl)quinazolin-2-yl)-N-methylbenzamide (UM0152893) displayed sub-micromolar enzyme inhibition of sEH.
2024-05-02
ArXiv (prépublication)
doi.org
arxiv.org