8 papers · 1 filter
Does Dimensionality Reduction via Random Projections Preserve Landscape Features?
Iván Olarte RodrÃguez, Anja Jankovic, Thomas Bäck +1
Exploratory Landscape Analysis (ELA) provides numerical features for characterizing black-box optimization problems. In high-dimensional settings, however, ELA suffers from sparsit…
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…
Mechanistic Interpretability for Transformer-based Time Series Classification
MatÄ«ss KalnÄre, 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…
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…
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…