About me
I am currently a research scientist at Apple MLR, broadly working in the area of Machine Learning theory. I just finished a short-term research visit at Toyota Technological Institute at Chicago (TTIC), and completed my postdoc stinct at Microsoft Research New York City before that. I obtained my PhD from 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: Machine Learning (esp. Online Learning theory, Bandits, Reinforcement Learning), Optimization, Federated Learning, Differential Privacy, Mechanism Design.My research focuses on developing large-scale robust algorithms for sequential decision-making tasks under restricted and unconventional feedback, for e.g., preference information, click data, proxy rewards, partial ranking, etc. Some of my past ventures also include handling complex prediction environments, like combinatorial decision spaces, dynamic regret, multiplayer games, distributed optimization, etc. Recently, I have also been interested in the interdisciplinary fields of prediction modeling with algorithmic fairness, assortment optimization and strategic mechanisms. Please feel free to reach out if you are interested in brainstorming any of these related directions!
Short Bio (in third person)
Aadirupa is currently a research scientist at Apple ML research, broadly working in the area of Machine Learning theory. She did a short-term research visit at Toyota Technological Institute, Chicago (TTIC), after finishing her postdoc at Microsoft Research New York City. Aadirupa obtained her Ph.D. from IISc Bangalore under Aditya Gopalan and Chiranjib Bhattacharyya.Her research primarily focuses on designing Efficient Human Aligned Prediction Models: Few specific research areas include Online learning theory, Bandits & RL, Federated Optimization, and Differential Privacy. Of late, she has also been working on some problems at the intersection of Mechanism Design, Game Theory and Algorithmic Fairness. Aadirupa has organized several workshops and tutorials in recent years, including a [NeurIPS, 2023] tutorial on Preference Learning, a [UAI, 2023] ] tutorial on Federated Optimization, two tutorials at [ECML, 2022] , [ACML, 2021], two ICML workshops [ICML, 2023] and [ICML, 2022], and two TTIC workshops [TTIC, 2023] and [TTIC, 2022]. She has also served in different panel discussions and reviewing committees.
[Selected Papers] [Full List] [Google Scholar] [DBLP] [arXiv]
Selected Papers:
- Eliciting User Preferences for Personalized Multi-Objective Decision Making through Comparative Feedback [Arxiv Version]
Han Shao, Lee Cohen, Avrim Blum, Yishay Mansour, Aadirupa Saha, Mathew Walter
In Neural Information Processing Systems, NeurIPS 2023 - Federated Online and Bandit Convex Optimization [Arxiv Version]
Kumar Kshitij Patel, Lingxiao Wang, Aadirupa Saha, Nathan Srebro
In International Conference on Machine Learning, ICML 2023 - ANACONDA: Improved Dynamic Regret Algorithm for Adaptive Non-Stationary Dueling Bandits [Arxiv Version]
Thomas Kleine Buening, Aadirupa Saha
In International Conference on Artificial Intelligence and Statistics, AIStats 2023 - Versatile Dueling Bandits [Arxiv Version]
Aadirupa Saha, Pierre Gaillard
In International Conference on Machine Learning, ICML 2022 - Efficient and Optimal Algorithms for Contextual Dueling Bandits under Realizability [Arxiv Version]
Aadirupa Saha, Akshay Krishnamurthy
In Algorithmic Learning Theory, ALT 2022 - 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 - 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 - 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