Alekh Agarwal

Researcher
Microsoft Research, New York
Email:


About Me

I am currently a researcher in the New York lab of Microsoft Research, where I also spent two wonderful years as a postdoc. Prior to that, 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.

Alex Slivkins and I are co-teaching a course on bandits and reinforcement learning at Columbia in Fall 2017. Check out the course webpage for lecture notes and other info!

Publications

Ph.D. Thesis
Preprints Journal Publications Conference Publications

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.
Area chair or equivalent: NIPS 2016, ICML 2016, ICML 2015, COLT 2015, ICML 2013, COLT 2013, AISTATS 2013, NIPS 2013.
Journal Reviewing: JMLR, Annals of Statistics, IEEE Transcations on Automatic Control, IEEE Transcations on Info Theory, SIAM Journal on Optimization, Machine Learning.

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