Selected Publications
See Google Scholar for a complete list.
See Google Scholar for a complete list.
Machine learning under distribution shifts: Methods for detecting, quantifying, and correcting distribution shifts.
L. Gruber, M. Holzleitner, S. Hochreiter, and W. Zellinger, “Minimax-optimal aggregation for density ratio estimation,” ICLR 2026
C. Hofmann, Ch. Huber, B. Lehner, D. Klotz, S. Hochreiter, and W. Zellinger, “AP-OOD: Attention pooling for out-of- distribution detection,” ICLR 2026
M.-C. Dinu, M. Holzleitner, M. Beck, D.H. Nguyen, A. Huber, H. Eghbal-zadeh, B. Moser, S. Pereverzyev, S. Hochreiter, and W. Zellinger, “Addressing parameter choice issues in unsupervised domain adaptation by aggregation,” ICLR (oral) 2023 [link]
W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, and S. Saminger-Platz, “Central moment discrepancy (CMD) for domain-invariant representation learning,” ICLR 2017 [link] [code]
Robustness assessment: Benchmarks, stress- testing, and evaluation protocols for reliable deployment.
S. Holly, A.-C. Zăvoianu, S. Silber, S. Hochreiter, and W. Zellinger, “The offline-frontier shift: Diagnosing distributional limits in generative multi-objective optimization,” ICLR Workshop on Science for Deep Learning 2026.
P. Setinek, G. Galletti, T. Gross, D. Schnürer, J. Brandstetter, and W. Zellinger, “SIMSHIFT: A benchmark for adapting neural surrogates to distribution shifts,” preprint 2025 [link]
K. Schweighofer, B. Brune, L. Gruber, S. Schmid, A. Aufreiter, A. Gruber, T. Doms, S. Eder, F. Mayer, X.-P. Stadlbauer, C. Schwald, W. Zellinger, B. Nessler, and S. Hochreiter, “Safe and certifiable AI systems: Concepts, challenges, and lessons learned,” TÜV AUSTRIA Report (industry white paper), 2025 [link]
Statistical learning theory: Principled learning methods with finite-sample and generalization guarantees.
W. Zellinger, “Binary losses for density ratio estimation,” ICLR 2025 [link] [code]
L. Gruber, M. Holzleitner, J. Lehner, S. Hochreiter, and W. Zellinger, “Overcoming saturation in density ratio estimation by iterated regularization,” ICML 2024.
W. Zellinger, S. Kindermann, and S. Pereverzyev, “Adaptive learning of density ratios in RKHS,” JMLR 2023 [link]
W. Zellinger, N. Shepeleva, M.-C. Dinu, H. Eghbal-zadeh, D.H. Nguyen, B. Nessler, S. Pereverzyev, and B.A. Moser, “The balancing principle for parameter choice in distance-regularized domain adaptation,” NeurIPS 2021 [link]
M. Holzleitner, S. Pereverzyev, and W. Zellinger, “Domain generalization by functional regression,” Numerical Functional Analysis and Optimization 2024.