25 citations · 153 across the 58 of their papers we have counts for
7 papers · 1 filter
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…
Hybrid Generative-Discriminative Models for Inverse Materials Design
Phuoc Nguyen, Truyen Tran, Sunil Gupta +2
Discovering new physical products and processes often demands enormous experimentation and expensive simulation. To design a new product with certain target characteristics, an ext…
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…
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…