23 citations · 57 across the 10 of their papers we have counts for
10 papers
Incorporating Expert Prior in Bayesian Optimisation via Space Warping
Anil Ramachandran, Sunil Gupta, Santu Rana +2
Bayesian optimisation is a well-known sample-efficient method for the optimisation of expensive black-box functions. However when dealing with big search spaces the algorithm goes…
Incorporating Expert Prior Knowledge into Experimental Design via Posterior Sampling
Cheng Li, Sunil Gupta, Santu Rana +3
Scientific experiments are usually expensive due to complex experimental preparation and processing. Experimental design is therefore involved with the task of finding the optimal…
Accelerating Experimental Design by Incorporating Experimenter Hunches
Cheng Li, Santu Rana, Sunil Gupta +8
Experimental design is a process of obtaining a product with target property via experimentation. Bayesian optimization offers a sample-efficient tool for experimental design when…
Sparse Spectrum Gaussian Process for Bayesian Optimization
Ang Yang, Cheng Li, Santu Rana +2
We propose a novel sparse spectrum approximation of Gaussian process (GP) tailored for Bayesian optimization. Whilst the current sparse spectrum methods provide desired approximati…
Practical Batch Bayesian Optimization for Less Expensive Functions
Vu Nguyen, Sunil Gupta, Santu Rana +2
Bayesian optimization (BO) and its batch extensions are successful for optimizing expensive black-box functions. However, these traditional BO approaches are not yet ideal for opti…
Accelerated Bayesian Optimization throughWeight-Prior Tuning
Alistair Shilton, Sunil Gupta, Santu Rana +10
Bayesian optimization (BO) is a widely-used method for optimizing expensive (to evaluate) problems. At the core of most BO methods is the modeling of the objective function using a…