[HTML][HTML] Gryffin: An algorithm for Bayesian optimization of categorical variables informed by expert knowledge
Designing functional molecules and advanced materials requires complex design choices:
tuning continuous process parameters such as temperatures or flow rates, while …
tuning continuous process parameters such as temperatures or flow rates, while …
Solving stochastic compositional optimization is nearly as easy as solving stochastic optimization
Stochastic compositional optimization generalizes classic (non-compositional) stochastic
optimization to the minimization of compositions of functions. Each composition may …
optimization to the minimization of compositions of functions. Each composition may …
Achieving robustness to aleatoric uncertainty with heteroscedastic Bayesian optimisation
Bayesian optimisation is a sample-efficient search methodology that holds great promise for
accelerating drug and materials discovery programs. A frequently-overlooked modelling …
accelerating drug and materials discovery programs. A frequently-overlooked modelling …
Are we forgetting about compositional optimisers in Bayesian optimisation?
Bayesian optimisation presents a sample-efficient methodology for global optimisation.
Within this framework, a crucial performance-determining subroutine is the maximisation of …
Within this framework, a crucial performance-determining subroutine is the maximisation of …
[PDF][PDF] Memory-based optimization methods for model-agnostic meta-learning
Recently, model-agnostic meta-learning (MAML) has garnered tremendous attention.
However, the stochastic optimization of MAML is still immature. Existing algorithms for MAML …
However, the stochastic optimization of MAML is still immature. Existing algorithms for MAML …