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 …
Max-value entropy search for efficient Bayesian optimization
Abstract Entropy Search (ES) and Predictive Entropy Search (PES) are popular and
empirically successful Bayesian Optimization techniques. Both rely on a compelling …
empirically successful Bayesian Optimization techniques. Both rely on a compelling …
A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization
Bayesian Optimization (BO), the application of Bayesian function approximation to finding
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
optima of expensive functions, has exploded in popularity in recent years. In particular, much …
Perspectives on the integration between first-principles and data-driven modeling
Efficiently embedding and/or integrating mechanistic information with data-driven models is
essential if it is desired to simultaneously take advantage of both engineering principles and …
essential if it is desired to simultaneously take advantage of both engineering principles and …
Tuning hyperparameters without grad students: Scalable and robust bayesian optimisation with dragonfly
K Kandasamy, KR Vysyaraju, W Neiswanger… - Journal of Machine …, 2020 - jmlr.org
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 …
Edge: Explaining deep reinforcement learning policies
With the rapid development of deep reinforcement learning (DRL) techniques, there is an
increasing need to understand and interpret DRL policies. While recent research has …
increasing need to understand and interpret DRL policies. While recent research has …
High dimensional Bayesian optimisation and bandits via additive models
K Kandasamy, J Schneider… - … conference on machine …, 2015 - proceedings.mlr.press
Bayesian Optimisation (BO) is a technique used in optimising a D-dimensional function
which is typically expensive to evaluate. While there have been many successes for BO in …
which is typically expensive to evaluate. While there have been many successes for BO in …
Structure discovery in nonparametric regression through compositional kernel search
Despite its importance, choosing the structural form of the kernel in nonparametric
regression remains a black art. We define a space of kernel structures which are built …
regression remains a black art. We define a space of kernel structures which are built …
Co-optimization of velocity planning and energy management for autonomous plug-in hybrid electric vehicles in urban driving scenarios
Co-optimization of vehicle velocity planning and powertrain control for plug-in hybrid electric
vehicle (PHEV) can lead to an optimal energy saving with the help of vehicle-to …
vehicle (PHEV) can lead to an optimal energy saving with the help of vehicle-to …