10 citations · 13 across the 10 of their papers we have counts for
10 papers
SMOG: Scalable Meta-Learning for Multi-Objective Bayesian Optimization
Leonard Papenmeier, Petru Tighineanu
Multi-objective optimization aims to solve problems with competing objectives. Evaluating such problems is often slow or expensive, limiting the budget of evaluations. In many appl…
Benchmarking that Matters: Rethinking Benchmarking for Practical Impact
Anna V. Kononova, Niki van Stein, Olaf Mersmann +14
Benchmarking has driven scientific progress in Evolutionary Computation, yet current practices fall short of real-world needs. Widely used synthetic suites such as BBOB and CEC iso…
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…
A Unified Framework for Entropy Search and Expected Improvement in Bayesian Optimization
Nuojin Cheng, Leonard Papenmeier, Stephen Becker +1
Bayesian optimization is a widely used method for optimizing expensive black-box functions, with Expected Improvement being one of the most commonly used acquisition functions. In…
High-dimensional Bayesian Optimization with Group Testing
Erik Orm Hellsten, Carl Hvarfner, Leonard Papenmeier +1
Bayesian optimization is an effective method for optimizing expensive-to-evaluate black-box functions. High-dimensional problems are particularly challenging as the surrogate model…
Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
Impactful applications such as materials discovery, hardware design, neural architecture search, or portfolio optimization require optimizing high-dimensional black-box functions w…
Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces
Leonard Papenmeier, Luigi Nardi, Matthias Poloczek
Recent advances have extended the scope of Bayesian optimization (BO) to expensive-to-evaluate black-box functions with dozens of dimensions, aspiring to unlock impactful applicati…