Kumar Krishna Agrawal

phd student at eecs, berkeley
fortunate to be advised by adam yala & trevor darrell.
previously ai research at google brain, math/cs at iit kharagpur.

algorithms & systems for human-centric machine learning.

about / github / gscholar

[teaching] humbled to be among outstanding gsi for 2022/2023
[blog] compute efficient model selection for self-supervised learning
[lecture] computer vision in practice at cs282 deep learning, uc berkeley

attribute diversity determines the systematicity gap in
visual-question answering
(in submission)
ian berlot-attwell, annabelle m. carrell, kka, yash sharma, naomi saphra

on the varied faces of model scaling in supervised and
self-supervised learning
(in submission)
matteo gamba, arna ghosh, kka, blake richards, hossein azizpour, marten bjorkman
openreview / website
in workshops on ssl theory & practice (oral), heavy tails & neurreps, neurips 2023

on addressing sample inefficiency in multi-view
representation learning
(in submission)
kka*, arna ghosh*, adam oberman, blake richards
workshop on ssl theory and practice, neurips 2023

falcon: live video analytics without profiling (in submission)
gur-eyal sela, kka, bharath balaji, joseph gonzalez, ion stoica

selected publications

neural population geometry across model scale: a tool for cross-species functional comparison of visual brain regions
arna ghosh, kka, zahraa chorghay, arnab kumar mondal, blake richards
computational and systems neuroscience, cosyne 2023

assessing representation quality in ssl by measuring eigenspectrum decay
arna ghosh*, kka*, arnab kumar mondal*, blake richards
neural information processing systems, neurips 2022
openreview / code / blog

octopus : low-latency & adaptive perception pipelines
gur-eyal sela, ionel gog, justin wong, kka, sukrit kalra, peter schafhalter, xiangxi mo,
xin wang, bharath balaji, ion stoica, joseph gonzalez
european conference in computer vision, eccv 2022
arxiv / code

learning from an exploring demonstrator: optimal reward estimation for bandits
wenshuo guo, kka, aditya grover, vidya muthukumar, ashwin pananjady
international conference on artificial intelligence and statistics aistats, 2022
workshop on theory of reinforcement learning, icml 2021
workshop on human-ai collaboration in sequential decision-making , icml 2021 (spotlight)

discrete flows: invertible generative models for discrete data
dustin tran, keyon vafa, kka, laurent dinh, ben poole
neural information processing systems neurips, 2019

gansynth: adversarial neural audio synthesis
jesse engel, kka, 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, kka, debidatta dwibedi, sergey levine, jonathan tompson
international conference on learning representations iclr 2019

towards mixed optimization for reinforcement learning
with program synthesis

surya bhupatiraju*, kka*, rishabh singh
workshop on neural abstract machines and program induction, icml 2018


Designing, Visualizing and Understanding Deep Neural Networks
fall 2022 @ ucberkeley

Introduction to Machine Learning
spring 2022, spring 2021 @ ucberkeley

Foundations of Machine Learning to Interact with a Dynamic World
fall 2021 @ ucberkeley

Depth First Learning : Learning to Understand Machine Learning
fellowship / whitepaper

(Template credits!)