Online Algorithms

A Reduction of Imitation Learning and Structured Prediction to No-Regret Online Learning AISTATS 2011 A simple multi-armed bandit algorithm with optimal variation-bounded regret COLT 2011 Bandits, Query Learning, and the Haystack Dimension COLT 2011 Budgeted Optimization with Concurrent Stochastic-Duration Experiments NeurIPS 2011 Committing Bandits NeurIPS 2011 Complexity-Based Approach to Calibration with Checking Rules COLT 2011 Contextual Bandits with Similarity Information COLT 2011 Efficient Online Learning via Randomized Rounding NeurIPS 2011 Follow-the-Regularized-Leader and Mirror Descent: Equivalence Theorems and L1 Regularization AISTATS 2011 From Bandits to Experts: On the Value of Side-Observations NeurIPS 2011 Improved Algorithms for Linear Stochastic Bandits NeurIPS 2011 Improved Regret Guarantees for Online Smooth Convex Optimization with Bandit Feedback AISTATS 2011 Learning Eigenvectors for Free NeurIPS 2011 Linear Submodular Bandits and their Application to Diversified Retrieval NeurIPS 2011 Minimax Policies for Combinatorial Prediction Games COLT 2011 Minimax Regret of Finite Partial-Monitoring Games in Stochastic Environments COLT 2011 Multi-armed bandits on implicit metric spaces NeurIPS 2011 Newtron: an Efficient Bandit algorithm for Online Multiclass Prediction NeurIPS 2011 Predicting Dynamic Difficulty NeurIPS 2011 Prediction strategies without loss NeurIPS 2011 Selecting the State-Representation in Reinforcement Learning NeurIPS 2011 Sparsity Regret Bounds for Individual Sequences in Online Linear Regression COLT 2011 The KL-UCB Algorithm for Bounded Stochastic Bandits and Beyond COLT 2011 Linear Algorithms for Online Multitask Classification JMLR 2010 New Adaptive Algorithms for Online Classification NeurIPS 2010