5 citations · 5 across the 2 of their papers we have counts for
6 papers · 1 filter
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…
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…
High Dimensional Bayesian Optimization Using Dropout
Cheng Li, Sunil Gupta, Santu Rana +3
Scaling Bayesian optimization to high dimensions is challenging task as the global optimization of high-dimensional acquisition function can be expensive and often infeasible. Exis…