3 papers
cs.LG2026
Unification and Optimization of Robust Supervised Learning
Jonas Hanselle, Valentin Margraf, Clemens Damke +1
The literature has proposed various robust alternatives to empirical risk minimisation to address failure modes such as distribution shift, label noise and finite-sample degeneraci…
cs.LG2025
Distribution Matching for Graph Quantification Under Structural Covariate Shift
Clemens Damke, Eyke Hüllermeier
Graphs are commonly used in machine learning to model relationships between instances. Consider the task of predicting the political preferences of users in a social network; to so…
cs.LG2025
Adjusted Count Quantification Learning on Graphs
Clemens Damke, Eyke Hüllermeier
Quantification learning is the task of predicting the label distribution of a set of instances. We study this problem in the context of graph-structured data, where the instances a…