Ilya Komarov
I worked as a particle physicist in 2012-2021. In 2016, I got my PhD in Physics from EPFL for analysis of data from LHCb experiment (CERN). After that, I changed the experiment, and joined Belle II collaboration for analysis of data collected on KEK collider in Japan.
In 2021, I changed my career and joined trivago as a Data Scientist to work on ranking problems.
In 2022 I joined Henkel, my current employer. I am working on several project including (but not limitig to) time series analysis, Bayesian Optimisation, and scheduling.
Henkel
Session
Bayesian optimization is a powerful technique for optimizing black-box, costly-to-evaluate functions, widely applicable across diverse fields. However, Gaussian process (GP) models commonly used in Bayesian optimization struggle with functions defined on categorical or mixed domains, limiting optimization in scenarios with numerous categorical inputs. In this talk, we present a solution by leveraging ensemble models for probabilistic modelling, providing a robust approach to optimize functions with categorical inputs. We showcase the effectiveness of our method through a Bayesian optimization setup implemented with the BoTorch library, utilizing probabilistic models from the XGBoostLSS framework. By integrating these tools, we achieve efficient optimization on domains with categorical variables, unlocking new possibilities for optimization in practical applications.