4 papers
DASH: Decoupled Adaptive Surrogate - Acquisition Harness for Automated Bayesian Optimization
Changquan Zhao, Yuxiang Sun, Ruihao Zhu +2
Bayesian optimization (BO) relies on a surrogate model and an acquisition function, yet the most suitable choices vary across tasks and optimization stages. Automated Bayesian opti…
Best-Arm Identification with Generative Proxy
Tianyi Ma, Hanzhang Qin, Ruihao Zhu +1
Best-arm identification is a canonical model for data-driven decision-making, but in many applications each reward observation is costly. Motivated by the growing availability of c…
Identifying All ε-Best Arms in (Misspecified) Linear Bandits
Zhekai Li, Tianyi Ma, Cheng Hua +1
Motivated by the need to efficiently identify multiple candidates in high trial-and-error cost tasks such as drug discovery, we propose a near-optimal algorithm to identify all ε-…
Satisficing Regret Minimization in Bandits: Constant Rate and Light-Tailed Distribution
Qing Feng, Tianyi Ma, Ruihao Zhu
Motivated by the concept of satisficing in decision-making, we consider the problem of satisficing regret minimization in bandit optimization. In this setting, the learner aims at…