NIPS Proceedings
^{β}
Books
Ambuj Tewari
23 Papers
Active Learning for Non-Parametric Regression Using Purely Random Trees
(2018)
But How Does It Work in Theory? Linear SVM with Random Features
(2018)
Action Centered Contextual Bandits
(2017)
Online multiclass boosting
(2017)
Phased Exploration with Greedy Exploitation in Stochastic Combinatorial Partial Monitoring Games
(2016)
Alternating Minimization for Regression Problems with Vector-valued Outputs
(2015)
Fighting Bandits with a New Kind of Smoothness
(2015)
Predtron: A Family of Online Algorithms for General Prediction Problems
(2015)
On Iterative Hard Thresholding Methods for High-dimensional M-Estimation
(2014)
Convex Calibrated Surrogates for Low-Rank Loss Matrices with Applications to Subset Ranking Losses
(2013)
Learning with Noisy Labels
(2013)
Feature Clustering for Accelerating Parallel Coordinate Descent
(2012)
Greedy Algorithms for Structurally Constrained High Dimensional Problems
(2011)
Nearest Neighbor based Greedy Coordinate Descent
(2011)
Online Learning: Stochastic, Constrained, and Smoothed Adversaries
(2011)
On the Universality of Online Mirror Descent
(2011)
Orthogonal Matching Pursuit with Replacement
(2011)
Online Learning: Random Averages, Combinatorial Parameters, and Learnability
(2010)
Smoothness, Low Noise and Fast Rates
(2010)
On the Complexity of Linear Prediction: Risk Bounds, Margin Bounds, and Regularization
(2008)
On the Generalization Ability of Online Strongly Convex Programming Algorithms
(2008)
Optimistic Linear Programming gives Logarithmic Regret for Irreducible MDPs
(2007)
Sample Complexity of Policy Search with Known Dynamics
(2006)