Synthesis of Programs from Multimodal Datasets. - Shantanu Thakoor; Simoni Shah; Ganesh Ramakrishnan; Amitabha Sanyal (AAAI, 2018)

The Marabou Framework for Verification and Analysis of Deep Neural Networks. - Guy Katz; Derek A. Huang; Duligur Ibeling; Kyle Julian; Christopher Lazarus; Rachel Lim; Parth Shah 003; Shantanu Thakoor; Haoze Wu 001; Aleksandar Zeljic; David L. Dill; Mykel J. Kochenderfer; et al. (CAV (1), 2019)

Counterfactual Credit Assignment in Model-Free Reinforcement Learning. - Thomas Mesnard; Théophane Weber; Fabio Viola; Shantanu Thakoor; Alaa Saade; Anna Harutyunyan; Will Dabney; Tom Stepleton; Nicolas Heess; Arthur Guez; Marcus Hutter; Lars Buesing; et al. (CoRR, 2020)

Counterfactual Credit Assignment in Model-Free Reinforcement Learning. - Thomas Mesnard; Theophane Weber; Fabio Viola; Shantanu Thakoor; Alaa Saade; Anna Harutyunyan; Will Dabney; Thomas S. Stepleton; Nicolas Heess; Arthur Guez; Eric Moulines; Marcus Hutter; et al. (ICML, 2021)

Geometric Entropic Exploration. - Zhaohan Daniel Guo; Mohammad Gheshlaghi Azar; Alaa Saade; Shantanu Thakoor; Bilal Piot; Bernardo Ávila Pires; Michal Valko; Thomas Mesnard; Tor Lattimore; Rémi Munos (CoRR, 2021)

Bootstrapped Representation Learning on Graphs. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Rémi Munos; Petar Velickovic; Michal Valko (CoRR, 2021)

Large-scale graph representation learning with very deep GNNs and self-supervision. - Ravichandra Addanki; Peter W. Battaglia; David Budden; Andreea Deac; Jonathan Godwin; Thomas Keck; Wai Lok Sibon Li; Alvaro Sanchez-Gonzalez; Jacklynn Stott; Shantanu Thakoor; Petar Velickovic (CoRR, 2021)

Quantifying and Understanding Adversarial Examples in Discrete Input Spaces. - Volodymyr Kuleshov; Evgenii Nikishin; Shantanu Thakoor; Tingfung Lau; Stefano Ermon (CoRR, 2021)

Large-Scale Representation Learning on Graphs via Bootstrapping. - Shantanu Thakoor; Corentin Tallec; Mohammad Gheshlaghi Azar; Mehdi Azabou; Eva L. Dyer; Rémi Munos; Petar Velickovic; Michal Valko (ICLR, 2022)

Generalised Policy Improvement with Geometric Policy Composition. - Shantanu Thakoor; Mark Rowland 001; Diana Borsa; Will Dabney; Rémi Munos; André Barreto 001 (ICML, 2022)

BYOL-Explore: Exploration by Bootstrapped Prediction. - Zhaohan Guo; Shantanu Thakoor; Miruna Pislar; Bernardo Ávila Pires; Florent Altché; Corentin Tallec; Alaa Saade; Daniele Calandriello; Jean-Bastien Grill; Yunhao Tang; Michal Valko; Rémi Munos; et al. (NeurIPS, 2022)

Learning Behavior Representations Through Multi-Timescale Bootstrapping. - Mehdi Azabou; Michael Mendelson; Maks Sorokin; Shantanu Thakoor; Nauman Ahad; Carolina Urzay; Eva L. Dyer (CoRR, 2022)

Half-Hop: A graph upsampling approach for slowing down message passing. - Mehdi Azabou; Venkataramana Ganesh; Shantanu Thakoor; Chi-Heng Lin; Lakshmi Sathidevi; Ran Liu; Michal Valko; Petar Velickovic; Eva L. Dyer (ICML, 2023)

Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. - Yash Chandak; Shantanu Thakoor; Zhaohan Daniel Guo; Yunhao Tang; Rémi Munos; Will Dabney; Diana L. Borsa (ICML, 2023)

Understanding Self-Predictive Learning for Reinforcement Learning. - Yunhao Tang; Zhaohan Daniel Guo; Pierre Harvey Richemond; Bernardo Ávila Pires; Yash Chandak; Rémi Munos; Mark Rowland 001; Mohammad Gheshlaghi Azar; Charline Le Lan; Clare Lyle; András György 001; Shantanu Thakoor; et al. (ICML, 2023)

Relax, it doesn't matter how you get there: A new self-supervised approach for multi-timescale behavior analysis. - Mehdi Azabou; Michael Mendelson; Nauman Ahad; Maks Sorokin; Shantanu Thakoor; Carolina Urzay; Eva L. Dyer (NeurIPS, 2023)

Gemma 2: Improving Open Language Models at a Practical Size. - Morgane Rivière; Shreya Pathak; Pier Giuseppe Sessa; Cassidy Hardin; Surya Bhupatiraju; Léonard Hussenot; Thomas Mesnard; Bobak Shahriari; Alexandre Ramé; Johan Ferret; Peter Liu; Pouya Tafti; et al. (CoRR, 2024)