Kumar Krishna Agrawal

I am a graduate student at EECS, UC Berkeley. Previously I was a researcher at Google Brain, part of the Google Brain Residency. 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.

Before this, I graduated from Indian Institute of Technology Kharagpur majoring 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.

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Kumar Krishna Agrawal
select publications

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

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

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

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

(This, is much more classy)