NIPS Proceedings
^{β}
Books
Sham M. Kakade
25 Papers
A Smoother Way to Train Structured Prediction Models
(2018)
Provably Correct Automatic Sub-Differentiation for Qualified Programs
(2018)
Learning Overcomplete HMMs
(2017)
Towards Generalization and Simplicity in Continuous Control
(2017)
Provable Efficient Online Matrix Completion via Non-convex Stochastic Gradient Descent
(2016)
Convergence Rates of Active Learning for Maximum Likelihood Estimation
(2015)
Super-Resolution Off the Grid
(2015)
When are Overcomplete Topic Models Identifiable? Uniqueness of Tensor Tucker Decompositions with Structured Sparsity
(2013)
A Spectral Algorithm for Latent Dirichlet Allocation
(2012)
Identifiability and Unmixing of Latent Parse Trees
(2012)
Learning Mixtures of Tree Graphical Models
(2012)
Efficient Learning of Generalized Linear and Single Index Models with Isotonic Regression
(2011)
Spectral Methods for Learning Multivariate Latent Tree Structure
(2011)
Stochastic convex optimization with bandit feedback
(2011)
Learning from Logged Implicit Exploration Data
(2010)
Multi-Label Prediction via Compressed Sensing
(2009)
Mind the Duality Gap: Logarithmic regret algorithms for online optimization
(2008)
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
(2008)
On the Generalization Ability of Online Strongly Convex Programming Algorithms
(2008)
The Price of Bandit Information for Online Optimization
(2007)
Economic Properties of Social Networks
(2004)
Experts in a Markov Decision Process
(2004)
Online Bounds for Bayesian Algorithms
(2004)
Policy Search by Dynamic Programming
(2003)
A Natural Policy Gradient
(2001)