Rahul Kidambi

I am a student of Prof. Sham M. Kakade studying Machine Learning at the University of Washington, Seattle.

I can be reached at rkidambi AT uw DOT edu.


My research centers around the design and analysis of scalable Algorithms for Machine Learning, as viewed through the lens of Optimization and Statistics. I am also interested in the design and implementation of large-scale Machine Learning Algorithms for problems involving structured input/output and non-convex optimization.

Previously, I worked on problems at the intersection of Structured Prediction, Semi-Supervised Learning and Active Learning, with applications to Missing Value Imputation and Active Estimation of a Machine Learning model's performance.

Selected Papers:

  • A Markov Chain Theory Approach to Characterizing the Minimax Optimality of Stochastic Gradient Descent (for Least Squares),
    with Prateek Jain, Sham M. Kakade, Praneeth Netrapalli, Venkata Krishna Pillutla and Aaron Sidford.
    ArXiv manuscript, abs/1710.09430, October 2017.
    To Appear in FSTTCS 2017.

  • Accelerating Stochastic Gradient Descent,
    with Prateek Jain, Sham M. Kakade, Praneeth Netrapalli and Aaron Sidford.
    ArXiv manuscript, abs/1704.08227, April 2017.
    Video: Sham at MSR.

  • Parallelizing Stochastic Approximation Through Mini-Batching and Tail Averaging,
    with Prateek Jain, Sham M. Kakade, Praneeth Netrapalli and Aaron Sidford.
    ArXiv manuscript, abs/1610.03774, October 2016.
    Accepted for journal publication pending minor revision, March 2017.

    The dblp listing provides a complete set of my papers.

    Academic Service:

  • Conference Reviewing: ISMB 2012, NIPS 2016, COLT 2017.
  • Journal Reviewing: Journal of Machine Learning Research (JMLR) 2015.


  • EE 514a: Information Theory-I (Autumn 2015).
  • EE 215: Fundamentals of Electrical Engineering (Autumn 2014, Winter 2015).

    Contact Information:

    Rahul Kidambi,
    Department of Electrical Engineering,
    185 Stevens Way, AE100R Campus Box 352500,
    University of Washington,
    Seattle, WA 98195-2500, USA.