Knowledge-integrated machine learning for materials: lessons from gameplaying and robotics
As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …
to wonder what lessons can be learned from other fields undergoing similar developments …
Recent advances in Bayesian optimization
Bayesian optimization has emerged at the forefront of expensive black-box optimization due
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
to its data efficiency. Recent years have witnessed a proliferation of studies on the …
Bayesian optimization for adaptive experimental design: A review
Bayesian optimisation is a statistical method that efficiently models and optimises expensive
“black-box” functions. This review considers the application of Bayesian optimisation to …
“black-box” functions. This review considers the application of Bayesian optimisation to …
Parallelised Bayesian optimisation via Thompson sampling
We design and analyse variations of the classical Thompson sampling (TS) procedure for
Bayesian optimisation (BO) in settings where function evaluations are expensive but can be …
Bayesian optimisation (BO) in settings where function evaluations are expensive but can be …
Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly
Bayesian Optimisation (BO) refers to a suite of techniques for global optimisation of
expensive black box functions, which use introspective Bayesian models of the function to …
expensive black box functions, which use introspective Bayesian models of the function to …