regret bound

Conditional Accelerated Lazy Stochastic Gradient Descent ICML 2017 Conservative Contextual Linear Bandits NeurIPS 2017 Contextual Bandits with Latent Confounders: An NMF Approach AISTATS 2017 Corralling a Band of Bandit Algorithms COLT 2017 Crowd Learning: Improving Online Decision Making Using Crowdsourced Data IJCAI 2017 Distributed Batch Gaussian Process Optimization ICML 2017 Dueling Bandits with Weak Regret ICML 2017 Efficient Online Bandit Multiclass Learning with Õ(√T) Regret ICML 2017 Efficient Online Linear Optimization with Approximation Algorithms NeurIPS 2017 Efficient Second-Order Online Kernel Learning with Adaptive Embedding NeurIPS 2017 Efficient Sublinear-Regret Algorithms for Online Sparse Linear Regression with Limited Observation NeurIPS 2017 Efficient tracking of a growing number of experts ALT 2017 Exploration-Exploitation in MDPs with Options AISTATS 2017 Fast rates for online learning in Linearly Solvable Markov Decision Processes COLT 2017 Following the Leader and Fast Rates in Online Linear Prediction: Curved Constraint Sets and Other Regularities JMLR 2017 Follow the Compressed Leader: Faster Online Learning of Eigenvectors and Faster MMWU ICML 2017 High Dimensional Bayesian Optimization using Dropout IJCAI 2017 Horde of Bandits using Gaussian Markov Random Fields AISTATS 2017 Identify the Nash Equilibrium in Static Games with Random Payoffs ICML 2017 Improving Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms and Its Applications NeurIPS 2017 Interactive Submodular Bandit NeurIPS 2017 Learning Linear Dynamical Systems via Spectral Filtering NeurIPS 2017 Learning Unknown Markov Decision Processes: A Thompson Sampling Approach NeurIPS 2017 Linear Thompson Sampling Revisited AISTATS 2017 Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization COLT 2017