Latest Academic Works
Representing and exploiting benchmarking data for optimisation and learning
The rapid advancements in Machine Learning (ML) and Black-Box Optimization (BBO) have led to an increased reliance on benchmarking data for evaluating and comparing algorithms across diverse domain tasks. However, the effective exploitation of this data is hindered by challenges such as syntactic variability, semantic ambiguity, and lack of standardization. …
Towards understanding the impact of problem landscapes in numerical black-box optimization
In optimization, it is well known that algorithm performance is dependent on the problem being solved. As a consequence of this, achieving good optimization results requires correctly matching an optimization problem to a specific optimization algorithm that performs well on that problem. For this to be possible, knowledge of both …
Exploiting domain knowledge in predictive learning from food and nutrition data
Human knowledge about food and nutrition has evolved drastically with time. With food and nutrition-related data being mass produced and easily accessible, the next step is to use Artificial Intelligence (AI) to translate data into knowledge. The majority of AI research is model-driven, and classical Machine Learning (ML) pipelines concentrate …