4 papers · 1 filter
Bencher: Simple and Reproducible Benchmarking for Black-Box Optimization
Leonard Papenmeier, Luigi Nardi
We present Bencher, a modular benchmarking framework for black-box optimization that fundamentally decouples benchmark execution from optimization logic. Unlike prior suites that f…
Leveraging Axis-Aligned Subspaces for High-Dimensional Bayesian Optimization with Group Testing
Erik Hellsten, Carl Hvarfner, Leonard Papenmeier +1
Bayesian optimization (BO ) is an effective method for optimizing expensive-to-evaluate black-box functions. While high-dimensional problems can be particularly challenging, due to…
Exploring Exploration in Bayesian Optimization
Leonard Papenmeier, Nuojin Cheng, Stephen Becker +1
A well-balanced exploration-exploitation trade-off is crucial for successful acquisition functions in Bayesian optimization. However, there is a lack of quantitative measures for e…
Understanding High-Dimensional Bayesian Optimization
Leonard Papenmeier, Matthias Poloczek, Luigi Nardi
Recent work reported that simple Bayesian optimization (BO) methods perform well for high-dimensional real-world tasks, seemingly contradicting prior work and tribal knowledge. Thi…