Bayesian Optimization, Gaussian Processes, Machine Learning, AutoML
Since September 2020, I'm a PhD student at the Wallenberg AI, Autonomous Systems and Software Program and the University of Lund Sweden.
I'm working on the optimization of black-box functions with Bayesian Optimization with a focus on high-dimensional functions with hundreds on input parameters.
I'm interested in exploring the relevance of black-box optimization and developing scalable and reliable algorithms for black-box optimization and applying them to real-world problems.
We present an algorithm for high-dimensional Bayesian Optimization using nested random embeddings (BAxUS). BAxUS starts the optimization in a very low-dimensional sparse embedding (typically only 1-3 dimensions) and increases the embedding as it optimizes. Using the sparse embedding, we can keep previous observations when increasing the embedding throughout the optimization.
|BoTorch tutorial on BAxUS:
|NeurIPS page (poster + short presentation):