347 citations · 365 across the 11 of their papers we have counts for
12 papers
Exposing Blind Spots in Deep Imbalanced Regression Evaluation
Noah C. Puetz, Jens U. Brandt, Marc Hilbert +3
Deep Imbalanced Regression (DIR) addresses a common failure mode of regression models: target distributions are highly non-uniform, causing models to perform best in densely popula…
Cascading CMA-ES Instances for Generating Input-diverse Solution Batches
Maria Laura Santoni, Christoph Dürr, Carola Doerr +2
Rather than obtaining a single good solution for a given optimization problem, users often seek alternative design choices, because the best-found solution may perform poorly with…
Illuminating the Diversity-Fitness Trade-Off in Black-Box Optimization
Maria Laura Santoni, Elena Raponi, Aneta Neumann +3
In real-world applications, users often favor structurally diverse design choices over one high-quality solution. It is hence important to consider more solutions that decision mak…
Pruning Federated Models through Loss Landscape Analysis and Client Agreement Scoring
Christian Internò, Elena Raponi, Markus Olhofer +5
The practical deployment of Federated Learning (FL) on resource-constrained devices is fundamentally limited by the high cost of training large models and the instability caused by…
Optimizing with Low Budgets: a Comparison on the Black-box Optimization Benchmarking Suite and OpenAI Gym
Elena Raponi, Nathanael Rakotonirina Carraz, Jérémy Rapin +2
The growing ubiquity of machine learning (ML) has led it to enter various areas of computer science, including black-box optimization (BBO). Recent research is particularly concern…
Self-Adjusting Weighted Expected Improvement for Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +2
Bayesian Optimization (BO) is a class of surrogate-based, sample-efficient algorithms for optimizing black-box problems with small evaluation budgets. The BO pipeline itself is hig…
Comparison of High-Dimensional Bayesian Optimization Algorithms on BBOB
Maria Laura Santoni, Elena Raponi, Renato De Leone +1
Bayesian Optimization (BO) is a class of black-box, surrogate-based heuristics that can efficiently optimize problems that are expensive to evaluate, and hence admit only small eva…
Towards Automated Design of Bayesian Optimization via Exploratory Landscape Analysis
Carolin Benjamins, Anja Jankovic, Elena Raponi +3
Bayesian optimization (BO) algorithms form a class of surrogate-based heuristics, aimed at efficiently computing high-quality solutions for numerical black-box optimization problem…
PI is back! Switching Acquisition Functions in Bayesian Optimization
Carolin Benjamins, Elena Raponi, Anja Jankovic +4
Bayesian Optimization (BO) is a powerful, sample-efficient technique to optimize expensive-to-evaluate functions. Each of the BO components, such as the surrogate model, the acquis…
High Dimensional Bayesian Optimization with Kernel Principal Component Analysis
Kirill Antonov, Elena Raponi, Hao Wang +1
Bayesian Optimization (BO) is a surrogate-based global optimization strategy that relies on a Gaussian Process regression (GPR) model to approximate the objective function and an a…
Quantum Annealing for Industry Applications: Introduction and Review
Sheir Yarkoni, Elena Raponi, Thomas Bäck +1
Quantum annealing is a heuristic quantum optimization algorithm that can be used to solve combinatorial optimization problems. In recent years, advances in quantum technologies hav…
High Dimensional Bayesian Optimization Assisted by Principal Component Analysis
Elena Raponi, Hao Wang, Mariusz Bujny +2
Bayesian Optimization (BO) is a surrogate-assisted global optimization technique that has been successfully applied in various fields, e.g., automated machine learning and design o…