← Optimization & Theory
Machine Learning › Optimization & Theory ›

Online Algorithms

803 directly classified papers

Papers per year

Papers

Multiarmed Bandits With Limited Expert Advice COLT 2014
A second-order bound with excess losses COLT 2014
Higher-Order Regret Bounds with Switching Costs COLT 2014
Online Linear Optimization via Smoothing COLT 2014
Follow the Leader with Dropout Perturbations COLT 2014
Lipschitz Bandits: Regret Lower Bound and Optimal Algorithms COLT 2014
Resourceful Contextual Bandits COLT 2014
Robust Multi-objective Learning with Mentor Feedback COLT 2014
Most Correlated Arms Identification COLT 2014
Bandit Convex Optimization: Towards Tight Bounds NIPS 2014
Towards Minimax Online Learning with Unknown Time Horizon ICML 2014
Three Guidelines of Online Learning for Large-Scale Visual Recognition CVPR 2014
Exploiting easy data in online optimization NIPS 2014
Prediction with Limited Advice and Multiarmed Bandits with Paid Observations ICML 2014
Boosting with Online Binary Learners for the Multiclass Bandit Problem ICML 2014
Taming the Monster: A Fast and Simple Algorithm for Contextual Bandits ICML 2014
Pseudo-reward Algorithms for Contextual Bandits with Linear Payoff Functions ACML 2014
A UCB-Like Strategy of Collaborative Filtering ACML 2014
Dual online inference for latent Dirichlet allocation ACML 2014
Online Non-Parametric Regression COLT 2014
Improved Bounds for Online Learning Over the Permutahedron and Other Ranking Polytopes AISTATS 2014
Set-Valued Approachability and Online Learning with Partial Monitoring JMLR 2014
LIBOL: A Library for Online Learning Algorithms JMLR 2014
Efficient Partial Monitoring with Prior Information NIPS 2014
Optimal Regret Minimization in Posted-Price Auctions with Strategic Buyers NIPS 2014