4 papers
Causal Explanation of Concept Drift -- A Truly Actionable Approach
David Komnick, Kathrin Lammers, Barbara Hammer +2
In a world that constantly changes, it is crucial to understand how those changes impact different systems, such as industrial manufacturing or critical infrastructure. Explaining…
Continuous Fair SMOTE -- Fairness-Aware Stream Learning from Imbalanced Data
Kathrin Lammers, Valerie Vaquet, Barbara Hammer
As machine learning is increasingly applied in an online fashion to deal with evolving data streams, the fairness of these algorithms is a matter of growing ethical and legal conce…
Conceptualizing Uncertainty: A Concept-based Approach to Explaining Uncertainty
Isaac Roberts, Alexander Schulz, Sarah Schroeder +2
Uncertainty in machine learning refers to the degree of confidence or lack thereof in a model's predictions. While uncertainty quantification methods exist, explanations of uncerta…
An Algorithm-Centered Approach To Model Streaming Data
Fabian Hinder, Valerie Vaquet, David Komnick +1
Besides the classical offline setup of machine learning, stream learning constitutes a well-established setup where data arrives over time in potentially non-stationary environment…