Aditya Modi

I am a PhD candidate at the Computer Science and Engineering department in University of Michigan, Ann Arbor. I'm fortunate to be advised by Satinder Singh and Ambuj Tewari.

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 settings. Additionally, I've an active interest in real-world applications of reinforcement learning methods.

Before coming to Michigan, I completed my undergraduate degree in Computer Science from Indian Institute of Technology, Kanpur in May 2016.

Recent News

• I will be visiting the Simons Institute for the Theory of RL program this Fall.
• One paper accepted to ICML 2020 (Project led by Shengpu Tang).
• Our paper on regret minimization in contextual reinforcement learning accepted to UAI 2020.
• Our paper on RL with model ensembles is accepted at AISTATS 2020!

Publications/Preprints

1. 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.

2. 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.

3. 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.

4. 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.

5. Markov Decision Processes with Continuous Side Information
   Aditya Modi, Nan Jiang, Satinder Singh, Ambuj Tewari
   Algorithmic Learning Theory (ALT) 2018.

Experience

• Microsoft Research, Redmond
  Research Intern, Adaptive Systems and Interaction group.
  July-October 2018.

• Microsoft Research, Bangalore
  Research Intern, Applied Sciences group.
  May-July 2015.

Service

• Reviewing: AAAI 2019, AISTATS 2019-2020, ICML 2019-20, NeurIPS 2019-20
• Organization: Statistical Machine Learning Reading Group (EECS,2017-18).

Teaching

• Introduction to Machine Learning - Winter 2017
  Prof. Jenna Wiens, University of Michigan