See [google scholar] for a full list.
A. Zimmel, G. Galletti, P. Setinek, J. Brandstetter, and W. Zellinger, “Towards Accurate Test-Time Adaptation for Neural Surrogates", NeurIPS AI4Science Workshop, 2025 [link]
W. Zellinger, “Binary losses for density ratio estimation,” ICLR, 2025 [link]
L. Gruber, M. Holzleitner, J. Lehner, S. Hochreiter, and W. Zellinger, “Overcoming saturation in density ratio estimation by iterated regularization,” ICML, 2024 [link]
D.H. Nguyen, W. Zellinger, and S. Pereverzyev, “On regularized Radon-Nikodym differentiation,” JMLR, 2024 [link]
W. Zellinger, S. Kindermann, and S. Pereverzyev, “Adaptive learning of density ratios in RKHS,” JMLR, 2023 [link]
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]
M. Holzleitner, S. Pereverzyev, and W. Zellinger, “Domain generalization by functional regression,” Numerical Functional Analysis and Optimization, 2023 [link]
W. Zellinger, N. Shepeleva, M.-C. Dinu, H. Eghbal-zadeh, H.D. Nguyen, B. Nessler, S. Pereverzyev, and B. Moser, “The balancing principle for parameter choice in distance-regularized domain adaptation,” NeurIPS, 2021 [link]
W. Zellinger, and B. Moser, “On the truncated Hausdorff moment problem under Sobolev regularity conditions,” Applied Mathematics and Computation, 2021 [link]
W. Zellinger, T. Grubinger, E. Lughofer, T. Natschläger, S. Saminger-Platz, "Central moment discrepancy (CMD) for domain-invariant representation learning," ICLR, 2017 [link]