Generative AI
Differential privacy
High dimensional statistics
Best subset selection
Robust Inference
Distributed learning
Reinforcement learning
Saptarshi Roy, Vansh Bansal, Purnamrita Sarkar, Alessandro Rinaldo (2025). 2-Rectifications are Enough for Straight Flows: A Theoretical Insight into Wasserstein Convergence
Subha Maity, Saptarshi Roy, Songkai Xue, Mikhail Yurochkin, Yuekai Sun (2025). How does overparametrization affect performance on minority groups? (Accepted in Transactions on Machine Learning Research with minor revision)
Saptarshi Roy, Ambuj Tewari, Ziwei Zhu (2025). Understanding Best Subset Selection: A Tale of Two C(omplex)ities (Accepted in Electronic Journal of Statistics)
Sunrit Chakraborty *, Saptarshi Roy *, Debabrota Basu (2024). FLIPHAT: Joint Differential Privacy for High Dimensional Sparse Linear Bandits (Accepted at AISTATS 2025, To appear) (* = Equal contribution)
Saptarshi Roy, Zehua Wang, Ambuj Tewari (2023). On the Computational Complexity of Private High-dimensional Model Selection via Exponential Mechanism (Accepted at Neurips 2024)
Ryan Swope , Amol Khanna , Philip Doldo , Saptarshi Roy , Edward Raff (2024). Feature Selection from Differentially Private Correlations (Accepted at 17th ACM Workshop on Artificial Intelligence and Security)
Saptarshi Roy, Ambuj Tewari, Ziwei Zhu (2024). High-dimensional variable selection with heterogeneous signals: A precise asymptotic perspective (Accepted in Bernoulli)
Sunrit Chakraborty *, Saptarshi Roy *, Ambuj Tewari (2023). Thompson Sampling for High-Dimensional Sparse Linear Contextual Bandits (Accepted in ICML 2023) (* = Equal contribution)
Saptarshi Roy, Kaustav Chakraborty, Somnath Bhadra, Ayanendranath Basu (2019). Density Power Downweighting and Robust Inference: Some New Strategies (Accepted in Journal of Mathematics and Statistics)
Saptarshi Roy (2024). Statistics in the Modern Era: High Dimensions, Decision-Making, and Privacy