4 papers
Cost-aware Stopping for Bayesian Optimization
Qian Xie, Linda Cai, Alexander Terenin +2
In automated machine learning, scientific discovery, and other applications of Bayesian optimization, deciding when to stop evaluating expensive black-box functions in a cost-aware…
The Gittins Index: A Design Principle for Decision-Making Under Uncertainty
Ziv Scully, Alexander Terenin
The Gittins index is a tool that optimally solves a variety of decision-making problems involving uncertainty, including multi-armed bandit problems, minimizing mean latency in que…
Cost-aware Bayesian Optimization via the Pandora's Box Gittins Index
Qian Xie, Raul Astudillo, Peter I. Frazier +2
Bayesian optimization is a technique for efficiently optimizing unknown functions in a black-box manner. To handle practical settings where gathering data requires use of finite re…
Multi-objective Bayesian optimization for design of Pareto-optimal current drive profiles in STEP
Theodore Brown, Stephen Marsden, Vignesh Gopakumar +3
The safety factor profile is a key property in determining the stability of tokamak plasmas. To design the safety factor profile in the United Kingdom's proposed Spherical Tokamak…