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