Online Algorithms

Improved Regret Bounds for Bandit Combinatorial Optimization NeurIPS 2019 Improvisation through Physical Understanding: Using Novel Objects As Tools with Visual Foresight RSS 2019 Incorporating Behavioral Constraints in Online AI Systems AAAI 2019 Indirect Trust is Simple to Establish IJCAI 2019 Large-Scale, Metric Structure From Motion for Unordered Light Fields CVPR 2019 Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems ICML 2019 Learning Linear-Quadratic Regulators Efficiently with only √T Regret ICML 2019 Learning Multiple Markov Chains via Adaptive Allocation NeurIPS 2019 Learning Optimal Strategies to Commit To AAAI 2019 Learning to Clear the Market ICML 2019 Learning to Correlate in Multi-Player General-Sum Sequential Games NeurIPS 2019 Learning to Prune: Speeding up Repeated Computations COLT 2019 Learning to Screen NeurIPS 2019 Learn Smart with Less: Building Better Online Decision Trees with Fewer Training Examples IJCAI 2019 Lifelong Optimization with Low Regret AISTATS 2019 Linear Stochastic Bandits Under Safety Constraints NeurIPS 2019 Lipschitz Adaptivity with Multiple Learning Rates in Online Learning COLT 2019 Logarithmic Regret for Online Gradient Descent Beyond Strong Convexity AISTATS 2019 Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing NeurIPS 2019 Making the Cut: A Bandit-based Approach to Tiered Interviewing NeurIPS 2019 Matrix-Free Preconditioning in Online Learning ICML 2019 MaxGap Bandit: Adaptive Algorithms for Approximate Ranking NeurIPS 2019 MaxHedge: Maximizing a Maximum Online AISTATS 2019 Meta-Descent for Online, Continual Prediction AAAI 2019 Model Selection for Contextual Bandits NeurIPS 2019