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Multi-fidelity Bayesian optimization in engineering design
Resided at the intersection of multi-fidelity optimization (MFO) and Bayesian optimization
(BO), MF BO has found a niche in solving expensive engineering design optimization …
(BO), MF BO has found a niche in solving expensive engineering design optimization …
Transition constrained Bayesian optimization via Markov decision processes
Bayesian optimization is a methodology to optimize black-box functions. Traditionally, it
focuses on the setting where you can arbitrarily query the search space. However, many real …
focuses on the setting where you can arbitrarily query the search space. However, many real …
No-regret learning of nash equilibrium for black-box games via Gaussian processes
This paper investigates the challenge of learning in black-box games, where the underlying
utility function is unknown to any of the agents. While there is an extensive body of literature …
utility function is unknown to any of the agents. While there is an extensive body of literature …
Variational Search Distributions
We develop variational search distributions (VSD), a method for finding discrete,
combinatorial designs of a rare desired class in a batch sequential manner with a fixed …
combinatorial designs of a rare desired class in a batch sequential manner with a fixed …
Multifidelity Bayesian Optimization: A Review
Resided at the intersection of multifidelity optimization (MFO) and Bayesian optimization
(BO), MF BO has found a niche in solving expensive engineering design optimization …
(BO), MF BO has found a niche in solving expensive engineering design optimization …
[PDF][PDF] Understanding and Improving Composite Bayesian Optimization
In this extended abstract, we consider Bayesian Optimization (BO) of composite objective
functions. By identifying a failure case of the existing composite BO method, we deepen the …
functions. By identifying a failure case of the existing composite BO method, we deepen the …