Portrait of Alekh Agarwal

Alekh Agarwal

Researcher

About

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.

I am broadly interested in Machine Learning and Reinforcement Learning. For more detailed information, please visit my personal webpage at http://alekhagarwal.net/.

 

Projects

Multiworld Testing

Established: November 1, 2013

Exponentially better than A/B testing. Multiworld Testing (MWT) is the capability to test and optimize over K policies (context-based decision rules) using an amount of data and computation that scales logarithmically in K, without…

Explore-Exploit Learning @MSR-NYC

Established: October 24, 2013

This is an umbrella project for machine learning with explore-exploit tradeoff: the trade-off between acquiring and using information. This is a mature, yet very active, research area studied in Machine Learning, Theoretical Computer Science, Operations Research, and Economics. Much of…

Publications

2016

2015

2014

2013

2012

2010

Projects

Other

Alekh Agarwal has been a co-organizer of the NIPS workshop on Optimization for Machine Learning from 2010 through 2015. He has been an area chair or equivalent for ICML (2013, 2015, & 2016), COLT (2013, 2015), AISTATS (2013), and NIPS (2013). He serves as a reviewed for several journals, including JMLR, Annals of Statistics, IEEE Transcations on Automatic Control, IEEE Transcations on Info Theory, SIAM Journal on Optimization, and Machine Learning.