Yash Chandak
Publication record assembled from the DBLP archive of ranked conferences.
Papers indexed
15
Venues
6
Active years
2019–2024
Best venue rank
A*
Where they publish
Papers
15 indexed papers, newest first.
| Year | Venue | Title | Authors |
|---|---|---|---|
| 2024 | AISTATS | A/B testing under Interference with Partial Network Information. | Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau |
| 2024 | EMNLP | Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. | Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Bill Wu, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist |
| 2024 | ICLR | Adaptive Instrument Design for Indirect Experiments. | Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill |
| 2024 | LAK | Estimating the Causal Treatment Effect of Unproductive Persistence. | Amelia Leon, Allen Nie, Yash Chandak, Emma Brunskill |
| 2023 | AISTATS | Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. | Vincent Liu, Yash Chandak, Philip S. Thomas, Martha White |
| 2023 | ICML | Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. | Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rmi Munos, Will Dabney, Diana L. Borsa |
| 2023 | ICML | Understanding Self-Predictive Learning for Reinforcement Learning. | Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo vila Pires, Yash Chandak, Rmi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, Andrs Gyrgy, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko |
| 2022 | AAAI | On Optimizing Interventions in Shared Autonomy. | Weihao Tan, David Koleczek, Siddhant Pradhan, Nicholas Perello, Vivek Chettiar, Vishal Rohra, Aaslesha Rajaram, Soundararajan Srinivasan, H. M. Sajjad Hossain, Yash Chandak |
| 2021 | AAAI | High-Confidence Off-Policy (or Counterfactual) Variance Estimation. | Yash Chandak, Shiv Shankar, Philip S. Thomas |
| 2021 | ICML | High Confidence Generalization for Reinforcement Learning. | James E. Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas |
| 2020 | AAAI | Reinforcement Learning When All Actions Are Not Always Available. | Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip S. Thomas |
| 2020 | AAAI | Lifelong Learning with a Changing Action Set. | Yash Chandak, Georgios Theocharous, Chris Nota, Philip S. Thomas |
| 2020 | ICML | Optimizing for the Future in Non-Stationary MDPs. | Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas |
| 2020 | ICML | Evaluating the Performance of Reinforcement Learning Algorithms. | Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas |
| 2019 | ICML | Learning Action Representations for Reinforcement Learning. | Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas |