19 citations · 22 across the 19 of their papers we have counts for
9 papers · 1 filter
Evolving Executable Pipeline Programs for AutoML with Language Models
Sofoklis Kitharidis, Cor J. Veenman, Jan N. van Rijn +2
Automated machine learning (AutoML) systems search for pipelines within a space of preprocessing operators, learners, and hyper-parameters specified in advance: they can select and…
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…
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…
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…
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…
TX-Gen: Multi-Objective Optimization for Sparse Counterfactual Explanations for Time-Series Classification
Qi Huang, Sofoklis Kitharidis, Thomas Bäck +1
In time-series classification, understanding model decisions is crucial for their application in high-stakes domains such as healthcare and finance. Counterfactual explanations, wh…
Shapelet-based Model-agnostic Counterfactual Local Explanations for Time Series Classification
Qi Huang, Wei Chen, Thomas Bäck +1
In this work, we propose a model-agnostic instance-based post-hoc explainability method for time series classification. The proposed algorithm, namely Time-CF, leverages shapelets…