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ICML
2010
IEEE

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Gaussian Process Optimization in the Bandit Setting: No Regret and Experimental Design

14 years 17 days ago

Many applications require optimizing an unknown, noisy function that is expensive to evaluate. We formalize this task as a multiarmed bandit problem, where the payoff function is ...

Niranjan Srinivas, Andreas Krause, Sham Kakade, Ma...

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olethrosPostdoctoral

EPFL

Homepage lia.epfl.ch

Bayesian Reinforcement Learning | Complexity Analysis | Decision Theory | Intrusion Detection | Learning In Games | Machine Learning | Partially Observable Stochastic Games | POMDPs | Regret Bounds | Reinforcement Learning | Stochastic Optimization |

posted by olethros Mar 14 2010

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Bayesian Reinforcement Learning | Complexity Analysis | Decision Theory | Intrusion Detection | Learning In Games | Machine Learning | Partially Observable Stochastic Games | POMDPs | Regret Bounds | Reinforcement Learning | Stochastic Optimization |

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