-
Asynchronous ε-Greedy Bayesian Optimisation
Bayesian Optimisation (BO) is a popular surrogate model-based approach f...
read it
-
Are we Forgetting about Compositional Optimisers in Bayesian Optimisation?
Bayesian optimisation presents a sample-efficient methodology for global...
read it
-
ε-shotgun: ε-greedy Batch Bayesian Optimisation
Bayesian optimisation is a popular, surrogate model-based approach for o...
read it
-
Achieving Robustness to Aleatoric Uncertainty with Heteroscedastic Bayesian Optimisation
Bayesian optimisation is an important decision-making tool for high-stak...
read it
-
Automatic Generation of Algorithms for Black-Box Robust Optimisation Problems
We develop algorithms capable of tackling robust black-box optimisation ...
read it
-
Evolutionary Optimisation of Real-Time Systems and Networks
The design space of networked embedded systems is very large, posing cha...
read it
-
Ecosystem-Oriented Distributed Evolutionary Computing
We create a novel optimisation technique inspired by natural ecosystems,...
read it
HEBO: Heteroscedastic Evolutionary Bayesian Optimisation
We introduce HEBO: Heteroscedastic Evolutionary Bayesian Optimisation that won the NeurIPS 2020 black-box optimisation competition. We present non-conventional modifications to the surrogate model and acquisition maximisation process and show such a combination superior against all baselines provided by the Bayesmark package. Lastly, we perform an ablation study to highlight the components that contributed to the success of HEBO.
READ FULL TEXT
Comments
There are no comments yet.