Alekh Agarwal

Microsoft Research, New York

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.

I am broadly interested in Machine Learning, Statistics and Optimization. My research focus is on problems which arise while applying machine learning techniques to massive datasets. Part of my research aims to understand the tradeoffs between learning and computation, as well as designing efficient learning algorithms that can learn under a given computational budget. On the algorithmic side, I am also quite interested in the design of distributed machine learning algorithms. Some of my other work considers computational and statistical aspects of estimation in high-dimensional problems. More recently, I have been looking at approaches for learning feature representations from data, in a theoretically principled and practically efficient manner. In a past life, I worked on Machine Learning applied to Web Search and Ranking.


Ph.D. Thesis
Preprints Journal Publications Conference Publications Refereed Workshop Publications
  • Extreme multi-class classification
    with Anna Choromanska and John Langford
    In NIPS 2013 Workshop on Extreme multiclass classification.

  • Learning Sparsely Used Overcomplete Dictionaries via Alternating Minimization
    with Anima Anandkumar, Prateek Jain, Praneeth Netrapalli and Rashish Tandon
    In NIPS 2013 OPT Workshop.

  • Exact Recovery of Sparsely Used Overcomplete Dictionaries
    with Anima Anandkumar and Praneeth Netrapalli
    In NIPS 2013 Workshop on spectral learning.

  • Stochastic optimization with non-i.i.d. noise
    with John Duchi
    In NIPS 2011 OPT Workshop.

  • Information-theoretic lower bounds on the oracle complexity of sparse convex optimization
    with Peter Bartlett, Pradeep Ravikumar and Martin Wainwright
    In NIPS 2010 OPT Workshop.

Professional Activities

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: 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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