3 papers
cs.LG2020
Exploiting Transitivity for Top-k Selection with Score-Based Dueling Bandits
Matthew Groves, Juergen Branke
We consider the problem of top-k subset selection in Dueling Bandit problems with score information. Real-world pairwise ranking problems often exhibit a high degree of transitivit…
stat.ML2020
Practical Bayesian Optimization of Objectives with Conditioning Variables
Michael Pearce, Janis Klaise, Matthew Groves
Bayesian optimization is a class of data efficient model based algorithms typically focused on global optimization. We consider the more general case where a user is faced with mul…
stat.ML2018
Efficient and Scalable Batch Bayesian Optimization Using K-Means
Matthew Groves, Edward O. Pyzer-Knapp
We present K-Means Batch Bayesian Optimization (KMBBO), a novel batch sampling algorithm for Bayesian Optimization (BO). KMBBO uses unsupervised learning to efficiently estimate pe…