Publications
Co-organizing 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.
Ph.D. Thesis
Preprints
Journal Publications
- Ergodic Subgradient Descent
with John Duchi, Mikael Johansson and Mike Jordan
To appear in SIAM Journal on Optimization.
- Fast global convergence of gradient methods for high-dimensional statistical recovery
with Sahand Negahban and Martin Wainwright
To appear in The Annals of Statistics.
- The Generalization Ability of Online Algorithms for Dependent Data
with John Duchi
To appear in IEEE Transactions on Information Theory.
- Stochastic convex optimization with bandit feedback
with Dean Foster, Daniel Hsu, Sham Kakade and Alexander Rakhlin
Accepted pending minor revision in SIAM Journal on Optimization.
- Noisy matrix decomposition via convex relaxation: Optimal rates in high dimensions (Annals formatted version)
with Sahand Negahban and Martin Wainwright
In The Annals of Statistics, Vol. 40, Number 2, July 2012.
- Information-theoretic lower bounds on the oracle complexity of stochastic convex optimization
with Peter Bartlett, Pradeep Ravikumar and Martin Wainwright
In IEEE Transcations on Information Theory, Vol 58, Issue 5, May 2012.
- Dual Averaging for Distributed Optimization: Convergence Analysis and Network Scaling
with John Duchi and Martin Wainwright
In IEEE Transactions on Automatic Control, Vol. 57, Issue 3, 2012.
- Message-passing for graph structured linear programs: Proximal projections, convergence and rounding schemes
with Pradeep Ravikumar and Martin Wainwright
In Journal Of Machine Learning Research, Vol. 11, 2010.
Conference Publications
- Selective sampling algorithms for cost-sensitive multiclass prediction (long version with proofs)
In ICML 2013
- Stochastic optimization and sparse statistical recovery: An optimal algorithm for high dimensions
with Sahand Negahban and Martin Wainwright
In NIPS 2012
- Contextual Bandit Learning with Predictable Rewards
with Miroslav Dudik, Satyen Kale, John Langford and Robert Schapire
In AISTATS 2012
- 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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