Sayak Ray Chowdhury

According to our database, Sayak Ray Chowdhury authored at least 28 papers between 2017 and 2024.

Collaborative distances:

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

2024

Communication Efficient Secure and Private Multi-Party Deep Learning.

Sankha Das, Sayak Ray Chowdhury, Nishanth Chandran, Divya Gupta, Satya Lokam, Rahul Sharma

IACR Cryptol. ePrint Arch., 2024

Right Now, Wrong Then: Non-Stationary Direct Preference Optimization under Preference Drift.

Seongho Son, William Bankes, Sayak Ray Chowdhury, Brooks Paige, Ilija Bogunovic

CoRR, 2024

Provably Sample Efficient RLHF via Active Preference Optimization.

Nirjhar Das, Souradip Chakraborty, Aldo Pacchiano, Sayak Ray Chowdhury

CoRR, 2024

OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification.

Shikhar Mohan, Deepak Saini, Anshul Mittal, Sayak Ray Chowdhury, Bhawna Paliwal, Jian Jiao, Manish Gupta, Manik Varma

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Provably Robust DPO: Aligning Language Models with Noisy Feedback.

Sayak Ray Chowdhury, Anush Kini, Nagarajan Natarajan

Proceedings of the Forty-first International Conference on Machine Learning, 2024

On Differentially Private Federated Linear Contextual Bandits.

Xingyu Zhou, Sayak Ray Chowdhury

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Differentially Private Reward Estimation with Preference Feedback.

Sayak Ray Chowdhury, Xingyu Zhou, Nagarajan Natarajan

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

2023

GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval.

Daman Arora, Anush Kini, Sayak Ray Chowdhury, Nagarajan Natarajan, Gaurav Sinha, Amit Sharma

CoRR, 2023

Combinatorial categorized bandits with expert rankings.

Sayak Ray Chowdhury, Gaurav Sinha, Nagarajan Natarajan, Amit Sharma

Proceedings of the Uncertainty in Artificial Intelligence, 2023

Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards.

Yulian Wu, Xingyu Zhou, Sayak Ray Chowdhury, Di Wang

Proceedings of the International Conference on Machine Learning, 2023

Distributed Differential Privacy in Multi-Armed Bandits.

Sayak Ray Chowdhury, Xingyu Zhou

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Bregman Deviations of Generic Exponential Families.

Sayak Ray Chowdhury, Patrick Saux, Odalric Maillard, Aditya Gopalan

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference.

Debangshu Banerjee, Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan

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

2022

Model Selection in Reinforcement Learning with General Function Approximations.

Avishek Ghosh, Sayak Ray Chowdhury

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2022

Shuffle Private Linear Contextual Bandits.

Sayak Ray Chowdhury, Xingyu Zhou

Proceedings of the International Conference on Machine Learning, 2022

Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning.

Sayak Ray Chowdhury, Rafael Oliveira

Proceedings of the Asian Conference on Machine Learning, 2022

Differentially Private Regret Minimization in Episodic Markov Decision Processes.

Sayak Ray Chowdhury, Xingyu Zhou

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

2021

Model Selection with Near Optimal Rates for Reinforcement Learning with General Model Classes.

Avishek Ghosh, Sayak Ray Chowdhury, Kannan Ramchandran

CoRR, 2021

Adaptive Control of Differentially Private Linear Quadratic Systems.

Sayak Ray Chowdhury, Xingyu Zhou, Ness B. Shroff

Proceedings of the IEEE International Symposium on Information Theory, 2021

Reinforcement Learning in Parametric MDPs with Exponential Families.

Sayak Ray Chowdhury, Aditya Gopalan, Odalric-Ambrym Maillard

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

No-regret Algorithms for Multi-task Bayesian Optimization.

Sayak Ray Chowdhury, Aditya Gopalan

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

No-Regret Reinforcement Learning with Value Function Approximation: a Kernel Embedding Approach.

Sayak Ray Chowdhury, Rafael Oliveira

CoRR, 2020

Active Learning of Conditional Mean Embeddings via Bayesian Optimisation.

Sayak Ray Chowdhury, Rafael Oliveira, Fabio Ramos

Proceedings of the Thirty-Sixth Conference on Uncertainty in Artificial Intelligence, 2020

2019

On Batch Bayesian Optimization.

Sayak Ray Chowdhury, Aditya Gopalan

CoRR, 2019

Bayesian Optimization under Heavy-tailed Payoffs.

Sayak Ray Chowdhury, Aditya Gopalan

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Online Learning in Kernelized Markov Decision Processes.

Sayak Ray Chowdhury, Aditya Gopalan

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2017

On Kernelized Multi-armed Bandits.

Sayak Ray Chowdhury, Aditya Gopalan

Proceedings of the 34th International Conference on Machine Learning, 2017

Misspecified Linear Bandits.

Avishek Ghosh, Sayak Ray Chowdhury, Aditya Gopalan

Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, 2017