63 citations · 230 across the 28 of their papers we have counts for
4 papers · 1 filter
Multi-Objective Bayesian Optimization with Active Preference Learning
Ryota Ozaki, Kazuki Ishikawa, Youhei Kanzaki +4
There are a lot of real-world black-box optimization problems that need to optimize multiple criteria simultaneously. However, in a multi-objective optimization (MOO) problem, iden…
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama +1
Among various acquisition functions (AFs) in Bayesian optimization (BO), Gaussian process upper confidence bound (GP-UCB) and Thompson sampling (TS) are well-known options with est…
Towards Practical Preferential Bayesian Optimization with Skew Gaussian Processes
Shion Takeno, Masahiro Nomura, Masayuki Karasuyama
We study preferential Bayesian optimization (BO) where reliable feedback is limited to pairwise comparison called duels. An important challenge in preferential BO, which uses the p…
Randomized Gaussian Process Upper Confidence Bound with Tighter Bayesian Regret Bounds
Shion Takeno, Yu Inatsu, Masayuki Karasuyama
Gaussian process upper confidence bound (GP-UCB) is a theoretically promising approach for black-box optimization; however, the confidence parameter is considerably large in th…