Yash Chandak

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 51

Top Topics

Reinforcement Learning

Markov Decision Processes

Reward Function

Top Venues

Publications

Amelia Leon, Allen Nie, Yash Chandak, Emma Brunskill
Estimating the Causal Treatment Effect of Unproductive Persistence. LAK (2024)
Yannis Flet-Berliac, Nathan Grinsztajn, Florian Strub, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Mohammad Gheshlaghi Azar, Olivier Pietquin, Matthieu Geist
Contrastive Policy Gradient: Aligning LLMs on sequence-level scores in a supervised-friendly fashion. CoRR (2024)
Allen Nie, Yash Chandak, Miroslav Suzara, Ali Malik, Juliette Woodrow, Matt Peng, Mehran Sahami, Emma Brunskill, Chris Piech
The GPT Surprise: Offering Large Language Model Chat in a Massive Coding Class Reduced Engagement but Increased Adopters Exam Performances. CoRR (2024)
Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau
A/B testing under Interference with Partial Network Information. CoRR (2024)
Hyunji Alex Nam, Yash Chandak, Emma Brunskill
Short-Long Policy Evaluation with Novel Actions. CoRR (2024)
Nathan Grinsztajn, Yannis Flet-Berliac, Mohammad Gheshlaghi Azar, Florian Strub, Bill Wu, Eugene Choi, Chris Cremer, Arash Ahmadian, Yash Chandak, Olivier Pietquin, Matthieu Geist
Averaging log-likelihoods in direct alignment. CoRR (2024)
Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill
Adaptive Instrument Design for Indirect Experiments. ICLR (2024)
Allen Nie, Yash Chandak, Christina J. Yuan, Anirudhan Badrinath, Yannis Flet-Berliac, Emma Brunskill
OPERA: Automatic Offline Policy Evaluation with Re-weighted Aggregates of Multiple Estimators. CoRR (2024)
Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau
A/B testing under Interference with Partial Network Information. AISTATS (2024)
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. CoRR (2023)
Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill
Adaptive Instrument Design for Indirect Experiments. CoRR (2023)
James E. Kostas, Scott M. Jordan, Yash Chandak, Georgios Theocharous, Dhawal Gupta, Martha White, Bruno Castro da Silva, Philip S. Thomas
Coagent Networks: Generalized and Scaled. CoRR (2023)
Vincent Liu, Yash Chandak, Philip S. Thomas, Martha White
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. CoRR (2023)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. ICML (2023)
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno Castro da Silva, Emma Brunskill, Philip S. Thomas
Off-Policy Evaluation for Action-Dependent Non-Stationary Environments. CoRR (2023)
Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno C. da Silva
Behavior Alignment via Reward Function Optimization. NeurIPS (2023)
Yash Chandak, Shantanu Thakoor, Zhaohan Daniel Guo, Yunhao Tang, Rémi Munos, Will Dabney, Diana L. Borsa
Representations and Exploration for Deep Reinforcement Learning using Singular Value Decomposition. ICML (2023)
Vincent Liu, Yash Chandak, Philip S. Thomas, Martha White
Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments. AISTATS (2023)
Yash Chandak, Shiv Shankar, Venkata Gandikota, Philip S. Thomas, Arya Mazumdar
Optimization using Parallel Gradient Evaluations on Multiple Parameters. CoRR (2023)
Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno Castro da Silva
Behavior Alignment via Reward Function Optimization. CoRR (2023)
Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
Supervised Pretraining Can Learn In-Context Reinforcement Learning. NeurIPS (2023)
Jonathan N. Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill
Supervised Pretraining Can Learn In-Context Reinforcement Learning. CoRR (2023)
Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno C. da Silva, Emma Brunskill, Philip S. Thomas
Off-Policy Evaluation for Action-Dependent Non-stationary Environments. NeurIPS (2022)
Yunhao Tang, Zhaohan Daniel Guo, Pierre Harvey Richemond, Bernardo Ávila Pires, Yash Chandak, Rémi Munos, Mark Rowland, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko
Understanding Self-Predictive Learning for Reinforcement Learning. CoRR (2022)
Weihao Tan, David Koleczek, Siddhant Pradhan, Nicholas Perello, Vivek Chettiar, Vishal Rohra, Aaslesha Rajaram, Soundararajan Srinivasan, H. M. Sajjad Hossain, Yash Chandak
On Optimizing Interventions in Shared Autonomy. AAAI (2022)
Tong Mu, Yash Chandak, Tatsunori B. Hashimoto, Emma Brunskill
Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits. NeurIPS (2022)
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Srinivasan Parthasarathy, Balaraman Ravindran
Scaling Graph Propagation Kernels for Predictive Learning. Frontiers Big Data 5 (2022)
Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum
SOPE: Spectrum of Off-Policy Estimators. CoRR (2021)
Yash Chandak, Scott Niekum, Bruno Castro da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas
Universal Off-Policy Evaluation. CoRR (2021)
Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum
SOPE: Spectrum of Off-Policy Estimators. NeurIPS (2021)
Yash Chandak, Shiv Shankar, Philip S. Thomas
High-Confidence Off-Policy (or Counterfactual) Variance Estimation. AAAI (2021)
Yash Chandak, Shiv Shankar, Philip S. Thomas
High-Confidence Off-Policy (or Counterfactual) Variance Estimation. CoRR (2021)
James Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas
High Confidence Generalization for Reinforcement Learning. ICML (2021)
Weihao Tan, David Koleczek, Siddhant Pradhan, Nicholas Perello, Vivek Chettiar, Vishal Rohra, Aaslesha Rajaram, Soundararajan Srinivasan, H. M. Sajjad Hossain, Yash Chandak
On Optimizing Interventions in Shared Autonomy. CoRR (2021)
Yash Chandak, Scott Niekum, Bruno C. da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas
Universal Off-Policy Evaluation. NeurIPS (2021)
Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip S. Thomas
Reinforcement Learning When All Actions Are Not Always Available. AAAI (2020)
Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
Towards Safe Policy Improvement for Non-Stationary MDPs. CoRR (2020)
Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas
Optimizing for the Future in Non-Stationary MDPs. CoRR (2020)
Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
Evaluating the Performance of Reinforcement Learning Algorithms. CoRR (2020)
Yash Chandak, Georgios Theocharous, Chris Nota, Philip S. Thomas
Lifelong Learning with a Changing Action Set. AAAI (2020)
Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas
Evaluating the Performance of Reinforcement Learning Algorithms. ICML (2020)
Georgios Theocharous, Yash Chandak, Philip S. Thomas, Frits de Nijs
Reinforcement Learning for Strategic Recommendations. CoRR (2020)
Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas
Towards Safe Policy Improvement for Non-Stationary MDPs. NeurIPS (2020)
Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas
Optimizing for the Future in Non-Stationary MDPs. ICML (2020)
Philip S. Thomas, Scott M. Jordan, Yash Chandak, Chris Nota, James Kostas
Classical Policy Gradient: Preserving Bellman's Principle of Optimality. CoRR (2019)
Yash Chandak, Georgios Theocharous, James Kostas, Scott M. Jordan, Philip S. Thomas
Learning Action Representations for Reinforcement Learning. CoRR (2019)
Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip S. Thomas
Reinforcement Learning When All Actions are Not Always Available. CoRR (2019)
Yash Chandak, Georgios Theocharous, Chris Nota, Philip S. Thomas
Lifelong Learning with a Changing Action Set. CoRR (2019)
Yash Chandak, Georgios Theocharous, James Kostas, Scott M. Jordan, Philip S. Thomas
Learning Action Representations for Reinforcement Learning. ICML (2019)
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Balaraman Ravindran
HOPF: Higher Order Propagation Framework for Deep Collective Classification. CoRR (2018)
Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Balaraman Ravindran
Fusion Graph Convolutional Networks. CoRR (2018)
Andreas Veit, Michael J. Wilber, Rajan Vaish, Serge J. Belongie, James Davis, Vishal Anand, Anshu Aviral, Prithvijit Chakrabarty, Yash Chandak, Sidharth Chaturvedi, Chinmaya Devaraj, Ankit Dhall, Utkarsh Dwivedi, Sanket Gupte, Sharath N. Sridhar, Karthik Paga, Anuj Pahuja, Aditya Raisinghani, Ayush Sharma, Shweta Sharma, Darpana Sinha, Nisarg Thakkar, K. Bala Vignesh, Utkarsh Verma, Kanniganti Abhishek, Amod Agrawal, Arya Aishwarya, Aurgho Bhattacharjee, Sarveshwaran Dhanasekar, Venkata Karthik Gullapalli, Shuchita Gupta, Chandana G, Kinjal Jain, Simran Kapur, Meghana Kasula, Shashi Kumar, Parth Kundaliya, Utkarsh Mathur, Alankrit Mishra, Aayush Mudgal, Aditya Nadimpalli, Munakala Sree Nihit, Akanksha Periwal, Ayush Sagar, Ayush Shah, Vikas Sharma, Yashovardhan Sharma, Faizal Siddiqui, Virender Singh, Abhinav S., Pradyumna Tambwekar, Rashida Taskin, Ankit Tripathi, Anurag D. Yadav
On Optimizing Human-Machine Task Assignments. CoRR (2015)