Humans as Information Sources in Bayesian Optimization

Humans are at the heart of the current computational revolution, not only as end-users, but also as integral contributors to computational systems such as machine learning (ML) solutions. This is because these systems depend on data that mainly originate from human activities, such as textual content, artistic creations, or transcribed audio clips. This data is not the only human-derived information flowing into the process, as human expertise plays an important role at all stages of ML development. This thesis reviews methodologies for expert knowledge elicitation, and delves into a promising approach to harnessing humans as a source of information, which is based on the following two ideas. The first idea is to assume the existence of a latent "intuition function" that describes an expert's knowledge over the problem of interest. The intuition function can only be accessed through queries that allow for human feedback, such as preferential queries. Learning the intuition function presents a tractable machine learning problem that can be approached through Gaussian process learning with a probabilistic user model on how the expert data is generated. The second idea pertains to how queries should be selected for an expert and how the expert's knowledge should be applied to the problem of interest. Multi-fidelity Bayesian optimization (MFBO) is a global optimization approach that incorporates multiple information sources with differing levels of accuracy and cost, accelerating the search for optimal solutions. Treating humans as auxiliary information sources within the MFBO framework effectively tackles issues concerning knowledge integration and sample-efficiency. This thesis addresses three problems that arise when humans serve as information sources in Bayesian optimization: (i) the requirement for natural human interaction, (ii) the inherent unreliability of human input, and (iii) the high cost associated with human labor. The articles included in the thesis present novel algorithms as viable solutions to the problems (i), (ii), and (iii). Specifically, we identify problem (ii) as an issue of negative transfer, and we provide an algorithm that establishes theoretical bounds on the negative transfer gap.