Misspecified Linear Bandits. - Avishek Ghosh; Sayak Ray Chowdhury; Aditya Gopalan (AAAI, 2017)

On Kernelized Multi-armed Bandits. - Sayak Ray Chowdhury; Aditya Gopalan (ICML, 2017)

Online Learning in Kernelized Markov Decision Processes. - Sayak Ray Chowdhury; Aditya Gopalan (AISTATS, 2019)

Bayesian Optimization under Heavy-tailed Payoffs. - Sayak Ray Chowdhury; Aditya Gopalan (NeurIPS, 2019)

On Batch Bayesian Optimization. - Sayak Ray Chowdhury; Aditya Gopalan (CoRR, 2019)

Active Learning of Conditional Mean Embeddings via Bayesian Optimisation. - Sayak Ray Chowdhury; Rafael Oliveira 001; Fabio Ramos 001 (UAI, 2020)

No-Regret Reinforcement Learning with Value Function Approximation: a Kernel Embedding Approach. - Sayak Ray Chowdhury; Rafael Oliveira 001 (CoRR, 2020)

No-regret Algorithms for Multi-task Bayesian Optimization. - Sayak Ray Chowdhury; Aditya Gopalan (AISTATS, 2021)

Reinforcement Learning in Parametric MDPs with Exponential Families. - Sayak Ray Chowdhury; Aditya Gopalan; Odalric-Ambrym Maillard (AISTATS, 2021)

Adaptive Control of Differentially Private Linear Quadratic Systems. - Sayak Ray Chowdhury; Xingyu Zhou 001; Ness B. Shroff (ISIT, 2021)

Model Selection with Near Optimal Rates for Reinforcement Learning with General Model Classes. - Avishek Ghosh; Sayak Ray Chowdhury; Kannan Ramchandran (CoRR, 2021)

Differentially Private Regret Minimization in Episodic Markov Decision Processes. - Sayak Ray Chowdhury; Xingyu Zhou 001 (AAAI, 2022)

Value Function Approximations via Kernel Embeddings for No-Regret Reinforcement Learning. - Sayak Ray Chowdhury; Rafael Oliveira 001 (ACML, 2022)

Shuffle Private Linear Contextual Bandits. - Sayak Ray Chowdhury; Xingyu Zhou 001 (ICML, 2022)

Model Selection in Reinforcement Learning with General Function Approximations. - Avishek Ghosh; Sayak Ray Chowdhury (ECML/PKDD (4), 2022)

Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference. - Debangshu Banerjee 002; Avishek Ghosh; Sayak Ray Chowdhury; Aditya Gopalan (AISTATS, 2023)

Bregman Deviations of Generic Exponential Families. - Sayak Ray Chowdhury; Patrick Saux; Odalric Maillard; Aditya Gopalan (COLT, 2023)

Distributed Differential Privacy in Multi-Armed Bandits. - Sayak Ray Chowdhury; Xingyu Zhou 001 (ICLR, 2023)

Differentially Private Episodic Reinforcement Learning with Heavy-tailed Rewards. - Yulian Wu; Xingyu Zhou 001; Sayak Ray Chowdhury; Di Wang 015 (ICML, 2023)

Combinatorial categorized bandits with expert rankings. - Sayak Ray Chowdhury; Gaurav Sinha 001; Nagarajan Natarajan; Amit Sharma (UAI, 2023)

GAR-meets-RAG Paradigm for Zero-Shot Information Retrieval. - Daman Arora; Anush Kini; Sayak Ray Chowdhury; Nagarajan Natarajan; Gaurav Sinha 001; Amit Sharma (CoRR, 2023)

Differentially Private Reward Estimation with Preference Feedback. - Sayak Ray Chowdhury; Xingyu Zhou 001; Nagarajan Natarajan (AISTATS, 2024)

On Differentially Private Federated Linear Contextual Bandits. - Xingyu Zhou 001; Sayak Ray Chowdhury (ICLR, 2024)

Provably Robust DPO: Aligning Language Models with Noisy Feedback. - Sayak Ray Chowdhury; Anush Kini; Nagarajan Natarajan (ICML, 2024)

OAK: Enriching Document Representations using Auxiliary Knowledge for Extreme Classification. - Shikhar Mohan; Deepak Saini; Anshul Mittal; Sayak Ray Chowdhury; Bhawna Paliwal; Jian Jiao 007; Manish Gupta; Manik Varma (ICML, 2024)

Provably Sample Efficient RLHF via Active Preference Optimization. - Nirjhar Das; Souradip Chakraborty; Aldo Pacchiano; Sayak Ray Chowdhury (CoRR, 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)

Communication Efficient Secure and Private Multi-Party Deep Learning. - Sankha Das; Sayak Ray Chowdhury; Nishanth Chandran; Divya Gupta 001; Satya Lokam; Rahul Sharma 001 (Proc. Priv. Enhancing Technol., 2025)

DP-NCB: Privacy Preserving Fair Bandits. - Dhruv Sarkar; Nishant Pandey; Sayak Ray Chowdhury (CoRR, 2025)