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 algorithms which are resource (data / compute) efficient and theoretically grounded. Broadly, I am interested in machine learning, robotics and languages (programming / natural). 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.

blog / github / gscholar / notes / bookshelf

Kumar Krishna Agrawal

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

Playing with Embeddings : Evaluating embeddings for Robot Language Learning through MUD Games
Anmol Gulati*, Kumar Krishna Agrawal*
Workshop on Evaluating Vector Space Representations, EMNLP 2017
arXiv / slides

Recurrent Memory Addressing for describing videos
Arnav Jain*, Abhinav Agarwalla*, Kumar Krishna Agrawal*, Pabitra Mitra
Workshop on Deep Learning in Computer Vision, CVPR 2017

(This, is much more classy)