regret bound

Artificial Constraints and Hints for Unbounded Online Learning COLT 2019 A simple parameter-free and adaptive approach to optimization under a minimal local smoothness assumption ALT 2019 AutoML from Service Provider’s Perspective: Multi-device, Multi-tenant Model Selection with GP-EI AISTATS 2019 Balanced Linear Contextual Bandits AAAI 2019 Bandit Online Learning with Unknown Delays AISTATS 2019 Bandit Principal Component Analysis COLT 2019 Bandits with Feedback Graphs and Switching Costs NeurIPS 2019 Batched Multi-armed Bandits Problem NeurIPS 2019 Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem COLT 2019 Bayesian Optimization under Heavy-tailed Payoffs NeurIPS 2019 Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously ICML 2019 Best of many worlds: Robust model selection for online supervised learning AISTATS 2019 Better Algorithms for Stochastic Bandits with Adversarial Corruptions COLT 2019 Bilinear Bandits with Low-rank Structure ICML 2019 Blocking Bandits NeurIPS 2019 Bootstrapping Upper Confidence Bound NeurIPS 2019 BubbleRank: Safe Online Learning to Re-Rank via Implicit Click Feedback UAI 2019 CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration ICML 2019 Cascading Non-Stationary Bandits: Online Learning to Rank in the Non-Stationary Cascade Model IJCAI 2019 Categorized Bandits NeurIPS 2019 Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints ICML 2019 Cleaning up the neighborhood: A full classification for adversarial partial monitoring ALT 2019 Combining Online Learning Guarantees COLT 2019 Connections Between Mirror Descent, Thompson Sampling and the Information Ratio NeurIPS 2019 Conservative Exploration using Interleaving AISTATS 2019