On Optimizing Human-Machine Task Assignments. - Andreas Veit; Michael J. Wilber; Rajan Vaish; Serge J. Belongie; James Davis 001; Vishal Anand 002; Anshu Aviral; Prithvijit Chakrabarty; Yash Chandak; Sidharth Chaturvedi; Chinmaya Devaraj; Ankit Dhall; et al. (CoRR, 2015)

HOPF: Higher Order Propagation Framework for Deep Collective Classification. - Priyesh Vijayan; Yash Chandak; Mitesh M. Khapra; Balaraman Ravindran (CoRR, 2018)

Fusion Graph Convolutional Networks. - Priyesh Vijayan; Yash Chandak; Mitesh M. Khapra; Balaraman Ravindran (CoRR, 2018)

Learning Action Representations for Reinforcement Learning. - Yash Chandak; Georgios Theocharous; James E. Kostas; Scott M. Jordan; Philip S. Thomas (ICML, 2019)

Classical Policy Gradient: Preserving Bellman's Principle of Optimality. - Philip S. Thomas; Scott M. Jordan; Yash Chandak; Chris Nota; James E. Kostas (CoRR, 2019)

Reinforcement Learning When All Actions Are Not Always Available. - Yash Chandak; Georgios Theocharous; Blossom Metevier; Philip S. Thomas (AAAI, 2020)

Lifelong Learning with a Changing Action Set. - Yash Chandak; Georgios Theocharous; Chris Nota; Philip S. Thomas (AAAI, 2020)

Optimizing for the Future in Non-Stationary MDPs. - Yash Chandak; Georgios Theocharous; Shiv Shankar; Martha White; Sridhar Mahadevan; Philip S. Thomas (ICML, 2020)

Evaluating the Performance of Reinforcement Learning Algorithms. - Scott M. Jordan; Yash Chandak; Daniel Cohen; Mengxue Zhang; Philip S. Thomas (ICML, 2020)

Towards Safe Policy Improvement for Non-Stationary MDPs. - Yash Chandak; Scott M. Jordan; Georgios Theocharous; Martha White; Philip S. Thomas (NeurIPS, 2020)

Reinforcement Learning for Strategic Recommendations. - Georgios Theocharous; Yash Chandak; Philip S. Thomas; Frits de Nijs (CoRR, 2020)

High-Confidence Off-Policy (or Counterfactual) Variance Estimation. - Yash Chandak; Shiv Shankar; Philip S. Thomas (AAAI, 2021)

High Confidence Generalization for Reinforcement Learning. - James E. Kostas; Yash Chandak; Scott M. Jordan; Georgios Theocharous; Philip S. Thomas (ICML, 2021)

Universal Off-Policy Evaluation. - Yash Chandak; Scott Niekum; Bruno C. da Silva 001; Erik G. Learned-Miller; Emma Brunskill; Philip S. Thomas (NeurIPS, 2021)

SOPE: Spectrum of Off-Policy Estimators. - Christina J. Yuan; Yash Chandak; Stephen Giguere 001; Philip S. Thomas; Scott Niekum (NeurIPS, 2021)

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 (AAAI, 2022)

Off-Policy Evaluation for Action-Dependent Non-stationary Environments. - Yash Chandak; Shiv Shankar; Nathaniel D. Bastian; Bruno C. da Silva 001; Emma Brunskill; Philip S. Thomas (NeurIPS, 2022)

Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits. - Tong Mu; Yash Chandak; Tatsunori B. Hashimoto; Emma Brunskill (NeurIPS, 2022)

Scaling Graph Propagation Kernels for Predictive Learning. - Priyesh Vijayan; Yash Chandak; Mitesh M. Khapra; Srinivasan Parthasarathy 001; Balaraman Ravindran (Frontiers Big Data, 2022)

Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. - Vincent Liu; Yash Chandak; Philip S. Thomas; Martha White (AISTATS, 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)

Supervised Pretraining Can Learn In-Context Reinforcement Learning. - Jonathan Lee 002; Annie Xie; Aldo Pacchiano; Yash Chandak; Chelsea Finn; Ofir Nachum; Emma Brunskill (NeurIPS, 2023)

Behavior Alignment via Reward Function Optimization. - Dhawal Gupta; Yash Chandak; Scott M. Jordan; Philip S. Thomas; Bruno C. da Silva 001 (NeurIPS, 2023)

Optimization using Parallel Gradient Evaluations on Multiple Parameters. - Yash Chandak; Shiv Shankar; Venkata Gandikota; Philip S. Thomas; Arya Mazumdar (CoRR, 2023)

Coagent Networks: Generalized and Scaled. - James E. Kostas; Scott M. Jordan; Yash Chandak; Georgios Theocharous; Dhawal Gupta; Martha White; Bruno Castro da Silva; Philip S. Thomas (CoRR, 2023)

A/B testing under Interference with Partial Network Information. - Shiv Shankar; Ritwik Sinha; Yash Chandak; Saayan Mitra; Madalina Fiterau (AISTATS, 2024)

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 (EMNLP, 2024)

Adaptive Instrument Design for Indirect Experiments. - Yash Chandak; Shiv Shankar; Vasilis Syrgkanis; Emma Brunskill (ICLR, 2024)

Estimating the Causal Treatment Effect of Unproductive Persistence. - Amelia Leon; Allen Nie; Yash Chandak; Emma Brunskill (LAK, 2024)

OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators. - Allen Nie; Yash Chandak; Christina J. Yuan; Anirudhan Badrinath; Yannis Flet-Berliac; Emma Brunskill (NeurIPS, 2024)

Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. - Yannis Flet-Berliac; Nathan Grinsztajn; Florian Strub; Eugene Choi; Chris Cremer; Arash Ahmadian; Yash Chandak; Mohammad Gheshlaghi Azar; Olivier Pietquin; Matthieu Geist (CoRR, 2024)

Averaging log-likelihoods in direct alignment. - Nathan Grinsztajn; Yannis Flet-Berliac; Mohammad Gheshlaghi Azar; Florian Strub; Bill Wu; Eugene Choi; Chris Cremer; Arash Ahmadian; Yash Chandak; Olivier Pietquin; Matthieu Geist (CoRR, 2024)

Short-Long Policy Evaluation with Novel Actions. - Hyunji Alex Nam; Yash Chandak; Emma Brunskill (CoRR, 2024)

The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances. - Allen Nie; Yash Chandak; Miroslav Suzara; Ali Malik; Juliette Woodrow; Matt Peng; Mehran Sahami; Emma Brunskill; Chris Piech (CoRR, 2024)

Command A: An Enterprise-Ready Large Language Model. - Aakanksha; Arash Ahmadian; Marwan Ahmed; Jay Alammar; Milad Alizadeh; Yazeed Alnumay; Sophia Althammer; Arkady Arkhangorodsky; Viraat Aryabumi; Dennis Aumiller; Raphaël Avalos; Zahara Aviv; et al. (CoRR, 2025)