5 citations · 5 across the 4 of their papers we have counts for
5 papers
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…
Bayesian functional optimisation with shape prior
Pratibha Vellanki, Santu Rana, Sunil Gupta +4
Real world experiments are expensive, and thus it is important to reach a target in minimum number of experiments. Experimental processes often involve control variables that chang…
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…
Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian Optimisation
Alistair Shilton, Sunil Gupta, Santu Rana +9
The paper presents a novel approach to direct covariance function learning for Bayesian optimisation, with particular emphasis on experimental design problems where an existing cor…
Rapid Bayesian optimisation for synthesis of short polymer fiber materials
Cheng Li, David Rubin de Celis Leal, Santu Rana +6
The discovery of processes for the synthesis of new materials involves many decisions about process design, operation, and material properties. Experimentation is crucial but as co…