Alekh Agarwal

Staff Research Scientist
Google
Email: ,


About Me

I am currently a researcher in the learning theory team at Google. Prior to that, I spent nine wonderful years at Microsoft Research where I was a member of the Machine Learning group in the New York lab and later led the Reinforcement Learning team in Redmond. I obtained my PhD in Computer Science from UC Berkeley, working with Peter Bartlett and Martin Wainwright.

Interests
I am broadly interested in Machine Learning, Statistics and Optimization. I am currently working on several aspects of Interactive Machine Learning, including contextual bandits, reinforcement learning and active learning with an eye towards practical learning systems with strong theoretical guarantees. I have previously worked on tradeoffs between computational and statistical complexities, large-scale and distributed machine learning and statistical inference in high-dimensions.


Publications

RL theory monograph: A monograph on RL theory based on notes from courses taught by Nan Jiang at UIUC and together with Sham Kakade at UW. The notes are being actively updated, and any feedback, typos etc. are welcome.

Ph.D. Thesis
Recent preprints Journal Publications Conference Publications

Teaching

CSE 599: Reinforcement Learning and Bandits, taught at University of Washington in Spring 2019 with Sham Kakade.
Bandits and Reinforcement Learning, taught at Columbia University in Fall 2017 with Alex Slivkins.


Professional Activities

Fundraising Chair for AISTATS 2016.
Co-organized NIPS 2015 workshop on Optimization for Machine Learning.
Co-organized NIPS 2014 workshop on Optimization for Machine Learning.
Co-organized NIPS 2013 workshop on Optimization for Machine Learning.
Co-organized NIPS 2013 workshop on Optimization for Machine Learning.
Co-organized NIPS 2012 workshop on Optimization for Machine Learning.
Co-organized NIPS 2011 workshop on Computational Trade-offs in Statistical Learning.
Co-organized NIPS 2010 workshop Learning on Cores, Clusters and Clouds.
Senior Area Chair: NeurIPS 2019, NeurIPS 2020.
Area chair or equivalent: ICML 2013-2020, NeurIPS 2013-2018, COLT 2013-2020, AISTATS 2013, NeurIPS 2013.
Journal Reviewing: JMLR, Annals of Statistics, IEEE Transcations on Automatic Control, IEEE Transcations on Info Theory, SIAM Journal on Optimization, Machine Learning.