Contextual Bayesian Optimization

Contextual Bayesian Optimization (CBO) tackles the problem of optimizing complex, black-box functions that are influenced by external factors or "contexts." Current research focuses on developing robust algorithms, such as primal-dual and violation-aware methods, that handle constraints, noisy data, and distributional shifts in the context, often employing Gaussian processes and techniques like mirror descent or ADMM. These advancements are improving the efficiency and reliability of CBO in diverse applications, including control systems optimization, resource management (e.g., energy consumption in buildings or wind energy systems), and combinatorial optimization problems, leading to more effective and data-efficient solutions.