International Conference on Machine Learning, 2017.Jose Miguel Hernandez-Lobato, Edward Pyzer-Knapp, Alan Aspuru-Guzik, and Ryan P Adams. Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space. In NIPS Workshop on Bayesian Optimization, 2016....
thompson-samplingepsilon-greedypolicy-evaluationmulti-armed-banditupper-confidence-bound UpdatedDec 1, 2020 Python Bayesian Optimization for Categorical and Continuous Inputs machine-learningoptimizationthompson-samplinghyperparameter-optimizationhyperoptgaussian-processesbayesian-optimizationmulti-armed-banditshyperparamet...
Optimistic Bayesian sampling in contextual-bandit problems Summary: In sequential decision problems in an unknown environment, the decision maker often faces a dilemma over whether to explore to discover more about... BC May,N Korda,A Lee,... - 《Journal of Machine Learning Research》 被引量:...
摘要: Thompson sampling is a randomized Bayesian machine learning method, whose original motivation was to sequentially evaluate treatments in clinical trials. In recent years, this method has drawn wide...关键词: revenue management dynamic pricing demand learning multiarmed bandit Thompson sampling ...
We address online combinatorial optimization when the player has a prior over the adversary's sequence of losses. In this setting, Russo and Van Roy proposed an information-theoretic analysis of Thompson Sampling based on the information ratio, allowing for elegant proofs of Bayesian regret bounds....
Viappiani, P. (2013). Thompson sampling for bayesian bandits with resets. In P. Perny, M. Pirlot, & A. TsoukiAăs (Eds.), Algorithmic decision theory (Vol. 8176, p. 399-410). Springer Berlin Heidelberg.Paolo Viappiani. "Thompson Sampling for Bayesian Bandits with Resets". In: ADT....
We compare different arm selection strategies with simulations, focusing on a Bayesian method based on Thompson sampling (a simple, yet effective, technique for trading off between exploration and exploitation).doi:10.1007/978-3-642-41575-3_31Paolo Viappiani...
Bayesian optimizationmachine learningMonte Carlo methodprotein engineeringThompson samplingAntibodies are one of the predominant treatment modalities for various diseases. To improve the characteristics of a lead antibody, such as antigen-binding affinity and stability, we conducted comprehensive substitutions ...
Thompson sampling is a heuristic algorithm for the multi-armed bandit problem which has a long tradition in machine learning. The algorithm has a Bayesian spirit in the sense that it selects arms based on posterior samples of reward probabilities of each arm. By forging a connection between ...
To do this, we reformulate Thompson sampling as an optimization proplem via the Gumbel-Max trick. After that we construct a set of random variables and our goal is to identify the one with highest mean which is an instance of best arm identification problems. Finally, we solve it with ...