Lars Schmidt-Thieme

FEML model improves predictions with limited data using knowledge transfer.

A new approach enhances vehicle routing solutions for larger problems using neural networks.

Moco uses machine learning to enhance combinatorial optimization problem solutions.

ProFITi model predicts outcomes from irregularly sampled time series.

ProbSAINT predicts used car prices while assessing uncertainty in predictions.

CDALBench offers a reliable testing ground for various Active Learning methods.