My research interests lie in developing methods with provable guarantees for interactive learning and sequential decision making frameworks like reinforcement learning, bandits and (general) online learning. My current focus is on the sample efficiency of reinforcement learning methods under various structural assumptions. Additionally, I've an active interest in real-world applications of reinforcement learning methods.
Model-free Representation Learning and Exploration in Low-rank MDPs
Aditya Modi*, Jinglin Chen*, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
* Equal contribution.
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Shengpu Tang, Aditya Modi, Michael Sjoding, Jenna Wiens
International Conference on Machine Learning (ICML), 2020.
No-regret Exploration in Contextual Reinforcement Learning
Aditya Modi and Ambuj Tewari
Conference on Uncertainty in Artificial Intelligence (UAI), 2020.
Abridged version accepted to ICML 2019 workshop on RL for Real Life and RLDM 2019.
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Aditya Modi, Nan Jiang, Ambuj Tewari, Satinder Singh
International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations
Aditya Modi, Debadeepta Dey, Alekh Agarwal, Adith Swaminathan, Besmira Nushi, Sean Andrist, Eric Horvitz
AAAI Conference on Artificial Intelligence (AAAI), 2020.
Invited poster, ICML 2019 Workshop on RL for Real Life.
Markov Decision Processes with Continuous Side Information
Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari
Algorithmic Learning Theory (ALT) 2018.
Microsoft Research, Redmond
Research Intern, Adaptive Systems and Interaction group.
Microsoft Research, Bangalore
Research Intern, Applied Sciences group.
Introduction to Machine Learning - Winter 2017
Prof. Jenna Wiens, University of Michigan