I'm an Applied Scientist at Microsoft AI. At Microsoft, I work on applied research problems in machine learning aimed at improving their advertising products. My research interests lie in general sequential decision making problems with a focus on the fundamental understanding and applications of interactive learning (reinforcement learning and bandits) and causal inference.
Before joining Microsoft, I finished my PhD at the Computer Science and Engineering department in University of Michigan, Ann Arbor. During my PhD, I worked on theoretical aspects of interactive learning and sequential decision making frameworks like reinforcement learning, bandits and (general) online learning. I was fortunate to be advised by Satinder Singh and Ambuj Tewari. My thesis developed provable sample efficient methods for reinforcement learning under various structural assumptions.
Towards Modular Machine Learning Pipelines
Aditya Modi, Jivat Kaur, Maggie Makar, Pavan Mallapragada, Amit Sharma, Emre Kiciman, Adith Swaminathan
Localized Learning Workshop, ICML '23.
On the Statistical Efficiency of Reward-free Exploration in Non-linear Reinforcement Learning
Jinglin Chen*, Aditya Modi*, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
Conference on Neural Information Processing Systems 2022 (NeurIPS '22 ).
* Equal contribution.
Multi-task Learning of Linear Control Systems under Instability
Aditya Modi, Ziping Xu, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari
ICML Workshop on Complex Feedback in Online Learning 2022 (ICML '22). Coming soon.
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems
Mohamad Kazem Shirani Faradonbeh, Aditya Modi
IFAC Workshop on Adaptive Learning and Control Systems 2022 (ALCoS '22 ).
Joint Learning of Linear Time-Invariant Dynamical Systems
Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari, George Michailidis
Accept provisionally as Regular Paper in Automatica.
Model-free Representation Learning and Exploration in Low-rank MDPs
Aditya Modi*, Jinglin Chen*, Akshay Krishnamurthy, Nan Jiang, Alekh Agarwal
Accepted to Journal of Machine Learning Research (JMLR) (under minor revisions). *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 2020 (ICML '20).
No-regret Exploration in Contextual Reinforcement Learning
Aditya Modi and Ambuj Tewari
Conference on Uncertainty in Artificial Intelligence 2020 (UAI '20).
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 2020 (AIStats '20).
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 2020 (AAAI '20).
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 2018 (ALT '18).
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