Online Algorithms

Efficient Second Order Online Learning by Sketching NeurIPS 2016 Fast Convergence of Online Pairwise Learning Algorithms AISTATS 2016 Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities NeurIPS 2016 Gains and Losses are Fundamentally Different in Regret Minimization: The Sparse Case JMLR 2016 Hardness of Online Sleeping Combinatorial Optimization Problems NeurIPS 2016 Heteroscedastic Sequences: Beyond Gaussianity ICML 2016 Highly-Smooth Zero-th Order Online Optimization COLT 2016 Horizontally Scalable Submodular Maximization ICML 2016 Importance Weighting Without Importance Weights: An Efficient Algorithm for Combinatorial Semi-Bandits JMLR 2016 Improved Regret Bounds for Oracle-Based Adversarial Contextual Bandits NeurIPS 2016 Instance-dependent Regret Bounds for Dueling Bandits COLT 2016 Large Scale Online Kernel Learning JMLR 2016 Learning Algorithms for Second-Price Auctions with Reserve JMLR 2016 Learning Simple Auctions COLT 2016 Linear Contextual Bandits with Knapsacks NeurIPS 2016 Machine Learning in an Auction Environment JMLR 2016 MetaGrad: Multiple Learning Rates in Online Learning NeurIPS 2016 Multi-armed Bandits: Competing with Optimal Sequences NeurIPS 2016 Multiple-Play Bandits in the Position-Based Model NeurIPS 2016 Multi-scale exploration of convex functions and bandit convex optimization COLT 2016 Non-stochastic Best Arm Identification and Hyperparameter Optimization AISTATS 2016 No-Regret Algorithms for Heavy-Tailed Linear Bandits ICML 2016 No Regret Bound for Extreme Bandits AISTATS 2016 OLPS: A Toolbox for On-Line Portfolio Selection JMLR 2016 On Explore-Then-Commit strategies NeurIPS 2016