86 citations · 471 across the 33 of their papers we have counts for
13 papers · 1 filter
The Behavior and Convergence of Local Bayesian Optimization
Kaiwen Wu, Kyurae Kim, Roman Garnett +1
A recent development in Bayesian optimization is the use of local optimization strategies, which can deliver strong empirical performance on high-dimensional problems compared to t…
Local Bayesian optimization via maximizing probability of descent
Quan Nguyen, Kaiwen Wu, Jacob R. Gardner +1
Local optimization presents a promising approach to expensive, high-dimensional black-box optimization by sidestepping the need to globally explore the search space. For objective…
Nonmyopic Multiclass Active Search with Diminishing Returns for Diverse Discovery
Quan Nguyen, Roman Garnett
Active search is a setting in adaptive experimental design where we aim to uncover members of rare, valuable class(es) subject to a budget constraint. An important consideration in…
Nonmyopic Multifidelity Active Search
Quan Nguyen, Arghavan Modiri, Roman Garnett
Active search is a learning paradigm where we seek to identify as many members of a rare, valuable class as possible given a labeling budget. Previous work on active search has ass…
Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees
Shali Jiang, Daniel R. Jiang, Maximilian Balandat +3
Bayesian optimization is a sequential decision making framework for optimizing expensive-to-evaluate black-box functions. Computing a full lookahead policy amounts to solving a hig…
BINOCULARS for Efficient, Nonmyopic Sequential Experimental Design
Shali Jiang, Henry Chai, Javier Gonzalez +1
Finite-horizon sequential experimental design (SED) arises naturally in many contexts, including hyperparameter tuning in machine learning among more traditional settings. Computin…