conftrace_

regret bound

1926 papers

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RB

Co-occurring keywords

online learning (1777) multi-armed bandit (1098) contextual bandit (381) stochastic optimization (1060) reinforcement learning (4352) online algorithm (445) upper confidence bound (197) markov decision process (790) thompson sampling (237) online convex optimization (165)

Papers

Thompson Sampling for Linear-Quadratic Control Problems AISTATS 2017
Near Minimax Optimal Players for the Finite-Time 3-Expert Prediction Problem NIPS 2017
Exploration-Exploitation in MDPs with Options AISTATS 2017
Online Prediction with Selfish Experts NIPS 2017
Unifying PAC and Regret: Uniform PAC Bounds for Episodic Reinforcement Learning NIPS 2017
Optimal Posted-Price Mechanism in Microtask Crowdsourcing IJCAI 2017
Crowd Learning: Improving Online Decision Making Using Crowdsourced Data IJCAI 2017
High Dimensional Bayesian Optimization using Dropout IJCAI 2017
Affine-Invariant Online Optimization and the Low-rank Experts Problem NIPS 2017
Real-Time Bidding with Side Information NIPS 2017
An Improved Parametrization and Analysis of the EXP3++ Algorithm for Stochastic and Adversarial Bandits COLT 2017
Open Problem: First-Order Regret Bounds for Contextual Bandits COLT 2017
Learning Linear Dynamical Systems via Spectral Filtering NIPS 2017
Lower Bounds on Regret for Noisy Gaussian Process Bandit Optimization COLT 2017
On Equivalence of Martingale Tail Bounds and Deterministic Regret Inequalities COLT 2017
Fast rates for online learning in Linearly Solvable Markov Decision Processes COLT 2017
Time-Varying Gaussian Process Bandit Optimization AISTATS 2016
No Regret Bound for Extreme Bandits AISTATS 2016
Revealing Graph Bandits for Maximizing Local Influence AISTATS 2016
MetaGrad: Multiple Learning Rates in Online Learning NIPS 2016
Regret of Queueing Bandits NIPS 2016
Causal Bandits: Learning Good Interventions via Causal Inference NIPS 2016
Learning in Games: Robustness of Fast Convergence NIPS 2016
Following the Leader and Fast Rates in Linear Prediction: Curved Constraint Sets and Other Regularities NIPS 2016
Tracking Slowly Moving Clairvoyant: Optimal Dynamic Regret of Online Learning with True and Noisy Gradient ICML 2016