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Author Information
Aadirupa Saha (Indian Institute of Science (IISc), Bangalore)
Bio: Aadirupa Saha is currently a visiting faculty at Toyota Technological Institute at Chicago (TTIC). She obtained her PhD from the Department of Computer Science, Indian Institute of Science, Bangalore, advised by Aditya Gopalan and Chiranjib Bhattacharyya. She spent two years at Microsoft Research New York City as a postdoctoral researcher. During her PhD, Aadirupa interned at Microsoft Research, Bangalore, Inria, Paris, and Google AI, Mountain View. Her research interests include Bandits, Reinforcement Learning, Optimization, Learning theory, Algorithms. She has organized various workshops, tutorials and also served as a reviewer in top ML conferences. Research Interests: Machine Learning Theory (specifically Online Learning, Bandits, Reinforcement Learning), Optimization, Game Theory, Algorithms. She is recently interested in exploring ML problems at the intersection of Fairness, Privacy, Game theory and Mechanism design.
Aditya Gopalan (Indian Institute of Science)
More from the Same Authors

2022 Workshop: Complex feedback in online learning »
Rémy Degenne · Pierre Gaillard · Wouter Koolen · Aadirupa Saha 
2022 Poster: Versatile Dueling Bandits: Bestofboth World Analyses for Learning from Relative Preferences »
Aadirupa Saha · Pierre Gaillard 
2022 Spotlight: Versatile Dueling Bandits: Bestofboth World Analyses for Learning from Relative Preferences »
Aadirupa Saha · Pierre Gaillard 
2022 Poster: ActorCritic based Improper Reinforcement Learning »
Mohammadi Zaki · Avi Mohan · Aditya Gopalan · Shie Mannor 
2022 Spotlight: ActorCritic based Improper Reinforcement Learning »
Mohammadi Zaki · Avi Mohan · Aditya Gopalan · Shie Mannor 
2022 Poster: Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models »
Viktor Bengs · Aadirupa Saha · Eyke Hüllermeier 
2022 Poster: Optimal and Efficient Dynamic Regret Algorithms for NonStationary Dueling Bandits »
Aadirupa Saha · Shubham Gupta 
2022 Spotlight: Optimal and Efficient Dynamic Regret Algorithms for NonStationary Dueling Bandits »
Aadirupa Saha · Shubham Gupta 
2022 Spotlight: Stochastic Contextual Dueling Bandits under Linear Stochastic Transitivity Models »
Viktor Bengs · Aadirupa Saha · Eyke Hüllermeier 
2021 Poster: ConfidenceBudget Matching for Sequential Budgeted Learning »
Yonathan Efroni · Nadav Merlis · Aadirupa Saha · Shie Mannor 
2021 Poster: Adversarial Dueling Bandits »
Aadirupa Saha · Tomer Koren · Yishay Mansour 
2021 Spotlight: ConfidenceBudget Matching for Sequential Budgeted Learning »
Yonathan Efroni · Nadav Merlis · Aadirupa Saha · Shie Mannor 
2021 Spotlight: Adversarial Dueling Bandits »
Aadirupa Saha · Tomer Koren · Yishay Mansour 
2021 Poster: Optimal regret algorithm for Pseudo1d Bandit Convex Optimization »
Aadirupa Saha · Nagarajan Natarajan · Praneeth Netrapalli · Prateek Jain 
2021 Spotlight: Optimal regret algorithm for Pseudo1d Bandit Convex Optimization »
Aadirupa Saha · Nagarajan Natarajan · Praneeth Netrapalli · Prateek Jain 
2021 Poster: Dueling Convex Optimization »
Aadirupa Saha · Tomer Koren · Yishay Mansour 
2021 Spotlight: Dueling Convex Optimization »
Aadirupa Saha · Tomer Koren · Yishay Mansour 
2020 Poster: Improved Sleeping Bandits with Stochastic Action Sets and Adversarial Rewards »
Aadirupa Saha · Pierre Gaillard · Michal Valko 
2017 Poster: On Kernelized Multiarmed Bandits »
Sayak Ray Chowdhury · Aditya Gopalan 
2017 Talk: On Kernelized Multiarmed Bandits »
Sayak Ray Chowdhury · Aditya Gopalan