I am a visiting faculty at Toyota Technological Institute at Chicago (TTIC). I recently completed my postdoctoral stinct at Microsoft Research New York City. Before that I was a Ph.D. student at the department of Computer Science, Indian Institute of Science, Bangalore, advised by Aditya Gopalan and Chiranjib Bhattacharyya. I was fortunate to intern at Microsoft Research, Bangalore, Inria, Paris, and Google AI, Mountain View.
Research interests: Bandits, Reinforcement Learning, Optimization, Learning theory, Algorithms.My current research focuses on developing large-scale robust algorithms for sequential decision-making tasks under restricted feedback, for example, preference information, click data, proxy rewards, partial ranking, etc. Some of my other recent ventures also include handling non-stationarity in contextual environments, differential privacy, multiplayer games, online convex optimization. Broadly, I am fascinated by the scopes of learning from unconventional partial monitoring feedback and the gaps between theory and practice.
[Selected Papers] [Full List] [Google Scholar] [DBLP] [arXiv]Selected Papers:
- Dueling Bandits with Adversarial Sleeping [Arxiv Version]
Aadirupa Saha, Pierre Gaillard
In Neural Information Processing Systems, NeurIPS 2021 - Optimal Algorithms for Stochastic Contextual Dueling Bandits
Aadirupa Saha
In Neural Information Processing Systems, NeurIPS 2021 - Dueling Convex Optimization
Aadirupa Saha, Tomer Koren, Yishay Mansour
In International Conference on Machine Learning, ICML 2021 - Adversarial Dueling Bandits [Arxiv Version]
Aadirupa Saha, Tomer Koren, Yishay Mansour
In International Conference on Machine Learning, ICML 2021 - Optimal Regret Algorithm for Pseudo-1d Bandit Convex Optimization [Arxiv Version]
Aadirupa Saha, Nagarajan Natarajan, Praneeth Netrapalli, Prateek Jain
In International Conference on Machine Learning, ICML 2021 - From PAC to Instance-Optimal Sample Complexity in the Plackett-Luce Model [Arxiv Version]
Aadirupa Saha, Aditya Gopalan
In International Conference on Machine Learning, ICML 2020 - Best-item Learning in Random Utility Models with Subset Choices [Arxiv Version]
Aadirupa Saha, Aditya Gopalan
In International Conference on Artificial Intelligence and Statistics, AIStats 2020 - Combinatorial Bandits with Relative Feedback [Arxiv Version]
Aadirupa Saha, Aditya Gopalan
In Neural Information Processing Systems, NeurIPS 2019 - PAC Battling Bandits in the Plackett-Luce Model [Arxiv Version]
Aadirupa Saha, Aditya Gopalan
In Algorithmic Learning Theory, ALT 2019