Yash Chandak

According to our database, Yash Chandak authored at least 29 papers between 2015 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Estimating the Causal Treatment Effect of Unproductive Persistence.

Amelia Leon, Allen Nie, Yash Chandak, Emma Brunskill

Proceedings of the 14th Learning Analytics and Knowledge Conference, 2024

A/B testing under Interference with Partial Network Information.

Shiv Shankar, Ritwik Sinha, Yash Chandak, Saayan Mitra, Madalina Fiterau

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Adaptive Instrument Design for Indirect Experiments.

Yash Chandak, Shiv Shankar, Vasilis Syrgkanis, Emma Brunskill

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

Optimization using Parallel Gradient Evaluations on Multiple Parameters.

Yash Chandak, Shiv Shankar, Venkata Gandikota, Philip S. Thomas, Arya Mazumdar

CoRR, 2023

Behavior Alignment via Reward Function Optimization.

Dhawal Gupta, Yash Chandak, Scott M. Jordan, Philip S. Thomas, Bruno C. da Silva

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Supervised Pretraining Can Learn In-Context Reinforcement Learning.

Jonathan Lee, Annie Xie, Aldo Pacchiano, Yash Chandak, Chelsea Finn, Ofir Nachum, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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, Mohammad Gheshlaghi Azar, Charline Le Lan, Clare Lyle, András György, Shantanu Thakoor, Will Dabney, Bilal Piot, Daniele Calandriello, Michal Valko

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the International Conference on Machine Learning, 2023

Asymptotically Unbiased Off-Policy Policy Evaluation when Reusing Old Data in Nonstationary Environments.

Vincent Liu, Yash Chandak, Philip S. Thomas, Martha White

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Scaling Graph Propagation Kernels for Predictive Learning.

Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Srinivasan Parthasarathy, Balaraman Ravindran

Frontiers Big Data, 2022

Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits.

Tong Mu, Yash Chandak, Tatsunori B. Hashimoto, Emma Brunskill

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Off-Policy Evaluation for Action-Dependent Non-stationary Environments.

Yash Chandak, Shiv Shankar, Nathaniel D. Bastian, Bruno C. da Silva, Emma Brunskill, Philip S. Thomas

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

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

Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022

2021

SOPE: Spectrum of Off-Policy Estimators.

Christina J. Yuan, Yash Chandak, Stephen Giguere, Philip S. Thomas, Scott Niekum

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Universal Off-Policy Evaluation.

Yash Chandak, Scott Niekum, Bruno C. da Silva, Erik G. Learned-Miller, Emma Brunskill, Philip S. Thomas

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

High Confidence Generalization for Reinforcement Learning.

James E. Kostas, Yash Chandak, Scott M. Jordan, Georgios Theocharous, Philip S. Thomas

Proceedings of the 38th International Conference on Machine Learning, 2021

High-Confidence Off-Policy (or Counterfactual) Variance Estimation.

Yash Chandak, Shiv Shankar, Philip S. Thomas

Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021

2020

Reinforcement Learning for Strategic Recommendations.

Georgios Theocharous, Yash Chandak, Philip S. Thomas, Frits de Nijs

CoRR, 2020

Towards Safe Policy Improvement for Non-Stationary MDPs.

Yash Chandak, Scott M. Jordan, Georgios Theocharous, Martha White, Philip S. Thomas

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Evaluating the Performance of Reinforcement Learning Algorithms.

Scott M. Jordan, Yash Chandak, Daniel Cohen, Mengxue Zhang, Philip S. Thomas

Proceedings of the 37th International Conference on Machine Learning, 2020

Optimizing for the Future in Non-Stationary MDPs.

Yash Chandak, Georgios Theocharous, Shiv Shankar, Martha White, Sridhar Mahadevan, Philip S. Thomas

Proceedings of the 37th International Conference on Machine Learning, 2020

Lifelong Learning with a Changing Action Set.

Yash Chandak, Georgios Theocharous, Chris Nota, Philip S. Thomas

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

Reinforcement Learning When All Actions Are Not Always Available.

Yash Chandak, Georgios Theocharous, Blossom Metevier, Philip S. Thomas

Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence, 2020

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

Learning Action Representations for Reinforcement Learning.

Yash Chandak, Georgios Theocharous, James E. Kostas, Scott M. Jordan, Philip S. Thomas

Proceedings of the 36th International Conference on Machine Learning, 2019

2018

Fusion Graph Convolutional Networks.

Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Balaraman Ravindran

CoRR, 2018

HOPF: Higher Order Propagation Framework for Deep Collective Classification.

Priyesh Vijayan, Yash Chandak, Mitesh M. Khapra, Balaraman Ravindran

CoRR, 2018

2015

On Optimizing Human-Machine Task Assignments.

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

CoRR, 2015