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Kumar Krishna Agrawal
I am a graduate student at EECS, UC Berkeley. My research is motivated by
the need to design fast algorithms which are statistically/computationally efficient. Broadly, I am interested in foundations of machine learning, reinforcement learning. I enjoy bringing together insights from fundamental research
and algorithm design, to build sytems which work in the real-world.
Previously I was a researcher at Google Brain, part of the Google Brain Residency, after graduating from Indian Institute of
Technology Kharagpur where I majored in Mathematics and Computing. In the
past, I've been fortunate to work under the guidance of
Prof. Yoshua Bengio,
Prof. Raman Arora
and Prof. B. Sury.
/ home /
gscholar /
notes /
teaching
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Learning from an Exploring Demonstrator: Optimal Reward Estimation for Bandits
Wenshuo Guo, Kumar Krishna Agrawal, Aditya Grover, Vidya Muthukumar, Ashwin Pananjady
in submisssion
Workshop on Theory of Reinforcement Learning, ICML 2021
Workshop on Human-AI Collaboration in Sequential Decision-Making
, ICML 2021 (spotlight)
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Discrete Flows: Invertible Generative Models for Discrete Data
Dustin Tran, Keyon Vafa,
Kumar Krishna Agrawal, Laurent Dinh, Ben Poole
Neural Information Processing Systems (NeurIPS), 2019
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GANSynth: Adversarial Neural Audio
Synthesis
Jesse Engel,
Kumar Krishna Agrawal, Shuo Chen,
Ishaan
Gulrajani,
Chris Donahue,
Adam Roberts
International Conference on Learning Representations
(ICLR), 2019
arXiv / Magenta blog / samples
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Discriminator Actor Critic: Addressing sample
inefficiency and reward bias in Adversarial
Imitation Learning
Ilya Kostrikov,
Kumar Krishna Agrawal,
Debidatta Dwibedi,
Sergey Levine,
Jonathan Tompson
International Conference on Learning Representations
(ICLR), 2019
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Towards Mixed Optimization for Reinforcement Learning with
Program Synthesis
Surya Bhupatiraju*, Kumar Krishna Agrawal*,
Rishabh Singh
Workshop on Neural Abstract Machines and Program Induction,
ICML 2018
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