Interests
I am broadly interested in Learning Theory, Statistics and Convex Optimization. I am also extremely interested in computational issues in learning theory, by which I do not mean that I like to show that certain learning problems are hard! I am interested in understanding the tradeoffs between learning and computation, and coming up with efficient learning algorithms that can learn under a given computational budget. In a past life, I used to work in Machine Learning applied to Web Search and Ranking.
If you're interested in the state of art of English to Hindi machine translation, or just want a good laugh, check this out.
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Publications
Co-organizing NIPS 2011 workshop on Computational Trade-offs in Statistical Learning.
Co-organized NIPS 2010 workshop Learning on Cores, Clusters and Clouds.
Ph.D. Thesis
Preprints
Journal Publications
Conference Publications
- Stochastic convex optimization with bandit feedback
with Dean Foster, Daniel Hsu, Sham Kakade and Alexander Rakhlin
In NIPS 2011
- Distributed Delayed Stochastic Optimization
with John Duchi
In NIPS 2011
- Ergodic Subgradient Descent
with John Duchi, Mikael Johansson and Mike Jordan
In Allerton 2011
- Learning with Missing Features
with Afshin Rostamizadeh and Peter Bartlett
In UAI 2011
- Oracle inequalities for computationally budgeted model selection
with John Duchi, Peter Bartlett and Clement Levrard
In COLT 2011
- Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions
with Sahand Negahban and Martin Wainwright
In ICML 2011
- 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.
- DIStributed Dual Averaging In Networks
with John Duchi and Martin Wainwright
In NIPS 2010.
- Convergence rates of gradient methods for high-dimensional statistical recovery
with Sahand Negahban and Martin Wainwright
In NIPS 2010.
- Optimal Algorithms for Online Convex Optimization with Multi-Point Bandit Feedback (longer version with additional proofs)
with Ofer Dekel and Lin Xiao
In COLT 2010.
- Optimal Allocation Strategies for the Dark Pool Problem
with Peter Bartlett and Max Dama
In AISTATS 2010.
- Information-theoretic lower bounds on the oracle complexity of convex optimization
with Peter Bartlett, Pradeep Ravikumar and Martin Wainwright
In NIPS 2009.
- A Stochastic View of Optimal Regret through Minimax Duality
with Jake Abernethy, Alexander Rakhlin and Peter Bartlett
arXiv preprint, short version appeared in COLT 2009.
- Message-passing for graph structured linear programs: Proximal projections, convergence and rounding schemes
with Pradeep Ravikumar and Martin Wainwright
In ICML 2008.
- An Analysis of Inference with the Universum
with Fabian Sinze, Olivier Chapelle and Bernhard Schölkopf
In NIPS 2007
- Learning Random Walks to Rank Nodes in Graphs
with Soumen Chakrabarti
In ICML 2007
- Learning Parameters in Entity-relationship Graphs from Ranking Preferences
with Soumen Chakrabarti
In ECML/PKDD 2006
- Learning to Rank Networked Entities
with Soumen Chakrabarti and Sunny Aggarwal
In SIGKDD 2006
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