Many real-world experimental design problems (a) evaluate multiple experimental
conditions in parallel and (b) replicate each condition multiple times due to large and …

Bayesian optimization (BO) is a widely-used sequential method for zeroth-order optimization
of complex and expensive-to-compute black-box functions. The existing BO methods …

Black box optimisation of an unknown function from expensive and noisy evaluations is a
ubiquitous problem in machine learning, academic research and industrial production. An …

Blockchain is the next generation of secure data management that creates near-immutable
decentralized storage. Secure cryptography created a niche for blockchain to provide …

Reinforcement learning utilizing kernel ridge regression to predict the expected value
function represents a powerful method with great representational capacity. This setting is a …

This paper introduces a framework for Bayesian Optimization (BO) with metric movement
costs, addressing a critical challenge in practical applications where input alterations incur …

In this study, we introduce an innovative approach to enhance interpretability in the design
optimization of voxel-based soft robots (VSRs). VSRs present a unique challenge in …

The present work deals with the algorithmic optimisation of return on investment in digital
Performance Marketing. Among the parameters that can be tuned, an advertiser must decide …

Blockchain is one of the fastest technologies that rivals the Internet in terms of adoption
speed. This security method is applicable to data-centric environments for validating data in …

Fluorescence decay of amino acids in protein is a complex process for which multiple
models have been proposed. Likelihood function evaluation for certain models can be …