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.

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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