most citedInternal Evaluation of Density-Based Clusterings with Noise

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cs.LG2026

RECAST: Model Reconstruction via Counterfactual-Aware Wasserstein Geometry under Limited Data

Xuan Zhao, Lena Krieger, Zhuo Cao +3

Counterfactual explanations (CFs) help understand machine learning models by identifying minimal input changes that would lead to alternative model outcomes. Recent work demonstrat…

cs.LG2026

Counterfactual Transport Flows for Offline Conservative Trajectory Refinement

Lena Krieger, Xuan Zhao, Zhuo Cao +3

Offline reinforcement learning (RL) offers a path to policy improvement from logged data alone, using historical returns or other measurable outcomes as world feedback. A key diffi…

cs.LG2026

ExDBSCAN: Explaining DBSCAN with Counterfactual Reasoning -- Additional Material

Pernille Matthews, Lena Krieger, Tommaso Amico +3

Clustering is an unsupervised technique for grouping data points by similarity. While explainability methods exist for supervised machine learning, they are not directly applicable…

cs.LG2025

DCFO: Density-Based Counterfactuals for Outliers -- Additional Material

Tommaso Amico, Pernille Matthews, Lena Krieger +2

Outlier detection identifies data points that significantly deviate from the majority of the data distribution. Explaining outliers is crucial for understanding the underlying fact…

cs.LG2025

LeapFactual: Reliable Visual Counterfactual Explanation Using Conditional Flow Matching

Zhuo Cao, Xuan Zhao, Lena Krieger +2

The growing integration of machine learning (ML) and artificial intelligence (AI) models into high-stakes domains such as healthcare and scientific research calls for models that a…

cs.LG2025

Galaxy Morphology Classification with Counterfactual Explanation

Zhuo Cao, Lena Krieger, Hanno Scharr +1

Galaxy morphologies play an essential role in the study of the evolution of galaxies. The determination of morphologies is laborious for a large amount of data giving rise to machi…

cs.LG2025★ 1 cited

Internal Evaluation of Density-Based Clusterings with Noise

Anna Beer, Lena Krieger, Pascal Weber +3

Being able to evaluate the quality of a clustering result even in the absence of ground truth cluster labels is fundamental for research in data mining. However, most cluster valid…