Tanmay Gangwani

I am a Ph.D. student in Computer Science at the University of Illinois, Urbana Champaign, supervised by Jian Peng. I'm interested in machine learning, especially Reinforcement Learning. My research is mainly focused on designing algorithms which efficiently leverage expert demonstrations for RL (imitation learning), address the exploration challenge in complex environment, and use generative modeling methods for model-based RL.

In past life, my research was at the intersection of Computer Architecture, Compilers and Systems. I earned my Master's degree from the same school, advised by Josep Torrellas. I have spent time at D-Wave, Uber AI, AMD and Intel.

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[To be uploaded soon] State-only Imitation with Transition Dynamics Mismatch
Tanmay Gangwani, Jian Peng
International Conference on Learning Representations (ICLR), 2020
Learning Belief Representations for Imitation Learning in POMDPs
Tanmay Gangwani, Joel Lehman, Qiang Liu, Jian Peng
The Conference on Uncertainty in Artificial Intelligence (UAI), 2019
code / bibtex
Learning Self-Imitating Diverse Policies
Tanmay Gangwani, Qiang Liu, Jian Peng
International Conference on Learning Representations (ICLR), 2019
code / slides / poster / bibtex
Policy Optimization by Genetic Distillation
Tanmay Gangwani, Jian Peng
International Conference on Learning Representations (ICLR), 2018
poster / bibtex
Distributed and Secure ML using Self-tallying Multi-party Aggregation
Yunhui Long*, Tanmay Gangwani*, Haris Mughees, Carl Gunter
(* denotes equal contribution)
NeurIPS workshop on Privacy Preserving Machine Learning (PPML), 2018
code / slides / bibtex
Breaking Serialization in Lock-Free Multicore Synchronization
Tanmay Gangwani, Adam Morrison, Josep Torrellas
Architectural Support for Programming Languages and Operating Systems (ASPLOS), 2016
poster / slides / bibtex
Partial Redundancy Elimination using Lazy Code Motion
Sandeep Dasgupta, Tanmay Gangwani
Technical Report, 2015
code / bibtex

Template imitation-learning (pun intended) using this!