Address: Microsoft Research,
300 Lafayette Street,
New York, NY 10012
Dipendra Misra
I am a Senior Researcher at Microsoft Research, New York.
I received my PhD in computer science from Cornell University (2019) and my
bachelors in computer science from Indian Institute of Technology Kanpur (2013).
Research Interests:
My main interest is in developing efficient machine learning algorithms with
applications to real-world problems. The word efficient here includes provable,
sample and computationally efficient, interpretable, scalable, and ethical.
My empirical focus is on problems in natural language understanding and allied fields.
I am currently active in reinforcement learning theory, interactive learning,
representation learning, and language and vision problems. I also have interest
in computational social science, and data and society.
News: We have two new provable reinforcement learning algorithms for rich-observation problems. These algorithms are computationally efficient, and their sample complexities do not depend on the size of observation space.
-
RichID at NeurIPS 2020 that provably solves continuous control problem
with latent LQR dynamics and rich-observations. RichID learns the latent LQR model and a near-optimal policy.
[arXiv Version] [Code] -
Homer at ICML 2020 that solves problems with a discrete latent state space. HOMER provably explores,
recovers the latent dynamics, and optimizes any given reward function.
[arXiv Version] [ICML Version] [Code] [Medium Post]
- For post-doc in machine learning at our New York lab apply here.
- For post-doc and full-time positions in reinforcement learning apply here.
Quick Links: MSR Reinforcement Learning, Cereb-RL Code Base, CIFF Code Base, My Blog, A Bandit Game, RL Formulas
