19 citations · 22 across the 19 of their papers we have counts for
10 papers · 1 filter
Mechanistic Interpretability for Transformer-based Time Series Classification
Matīss Kalnāre, Sofoklis Kitharidis, Thomas Bäck +1
Transformer-based models have become state-of-the-art tools in various machine learning tasks, including time series classification, yet their complexity makes understanding their…
MECHBench: A Set of Black-Box Optimization Benchmarks originated from Structural Mechanics
Iván Olarte Rodríguez, Maria Laura Santoni, Fabian Duddeck +3
Benchmarking is essential for developing and evaluating black-box optimization algorithms, providing a structured means to analyze their search behavior. Its effectiveness relies o…
Visual Model Selection using Feature Importance Clusters in Fairness-Performance Similarity Optimized Space
Sofoklis Kitharidis, Cor J. Veenman, Thomas Bäck +1
In the context of algorithmic decision-making, fair machine learning methods often yield multiple models that balance predictive fairness and performance in varying degrees. This d…
EvoCAD: Evolutionary CAD Code Generation with Vision Language Models
Tobias Preintner, Weixuan Yuan, Adrian König +3
Combining large language models with evolutionary computation algorithms represents a promising research direction leveraging the remarkable generative and in-context learning capa…
Leveraging Lightweight Generators for Memory Efficient Continual Learning
Christiaan Lamers, Ahmed Nabil Belbachir, Thomas Bäck +1
Catastrophic forgetting can be trivially alleviated by keeping all data from previous tasks in memory. Therefore, minimizing the memory footprint while maximizing the amount of rel…
Feasibility-Driven Trust Region Bayesian Optimization
Paolo Ascia, Elena Raponi, Thomas Bäck +1
Bayesian optimization is a powerful tool for solving real-world optimization tasks under tight evaluation budgets, making it well-suited for applications involving costly simulatio…
LLaMEA-BO: A Large Language Model Evolutionary Algorithm for Automatically Generating Bayesian Optimization Algorithms
Wenhu Li, Niki van Stein, Thomas Bäck +1
Bayesian optimization (BO) is a powerful class of algorithms for optimizing expensive black-box functions, but designing effective BO algorithms remains a manual, expertise-driven…
Why Are You Wrong? Counterfactual Explanations for Language Grounding with 3D Objects
Tobias Preintner, Weixuan Yuan, Qi Huang +4
Combining natural language and geometric shapes is an emerging research area with multiple applications in robotics and language-assisted design. A crucial task in this domain is o…
Large Language Models and Evolutionary Computation: A Critical Review of Bidirectional Interaction, Automated Algorithm Design, and Co-Adaptive Systems
Dikshit Chauhan, Bapi Dutta, Indu Bala +3
Large Language Models (LLMs) and Evolutionary Computation (EC) are increasingly being combined to support automated optimization, algorithm design, and adaptive decision-making. Th…
Diffusion and Flow Matching Models for Tabular Data: A Survey
Zhong Li, Qi Huang, Lincen Yang +5
Deep generative models have made rapid progress in image, text, audio, and video generation, and are increasingly being applied to structured records. For tabular data, however, ge…