Learning Sparse Latent Representations with the Deep Copula Information Bottleneck. - Aleksander Wieczorek; Mario Wieser; Damian Murezzan; Volker Roth 001 (ICLR (Poster), 2018)

Causal Deep Information Bottleneck. - Sonali Parbhoo; Mario Wieser; Volker Roth 001 (CoRR, 2018)

Informed MCMC with Bayesian Neural Networks for Facial Image Analysis. - Adam Kortylewski; Mario Wieser; Andreas Morel-Forster; Aleksander Wieczorek; Sonali Parbhoo; Volker Roth 001; Thomas Vetter (CoRR, 2018)

Estimating Causal Effects With Partial Covariates For Clinical Interpretability. - Sonali Parbhoo; Mario Wieser; Volker Roth 001 (CoRR, 2018)

Greedy Structure Learning of Hierarchical Compositional Models. - Adam Kortylewski; Aleksander Wieczorek; Mario Wieser; Clemens Blumer; Sonali Parbhoo; Andreas Morel-Forster; Volker Roth 001; Thomas Vetter (CVPR, 2019)

Deep Archetypal Analysis. - Sebastian Mathias Keller; Maxim Samarin; Mario Wieser; Volker Roth 001 (GCPR, 2019)

Transfer Learning from Well-Curated to Less-Resourced Populations with HIV. - Sonali Parbhoo; Mario Wieser; Volker Roth 001; Finale Doshi-Velez (MLHC, 2020)

Inverse Learning of Symmetries. - Mario Wieser; Sonali Parbhoo; Aleksander Wieczorek; Volker Roth 001 (NeurIPS, 2020)

Inverse Learning of Symmetry Transformations. - Mario Wieser; Sonali Parbhoo; Aleksander Wieczorek; Volker Roth 001 (CoRR, 2020)

3DMolNet: A Generative Network for Molecular Structures. - Vitali Nesterov; Mario Wieser; Volker Roth 001 (CoRR, 2020)

Information Bottleneck for Estimating Treatment Effects with Systematically Missing Covariates. - Sonali Parbhoo; Mario Wieser; Aleksander Wieczorek; Volker Roth 001 (Entropy, 2020)

Learning Conditional Invariance Through Cycle Consistency. - Maxim Samarin; Vitali Nesterov; Mario Wieser; Aleksander Wieczorek; Sonali Parbhoo; Volker Roth 001 (GCPR, 2021)

Learning Extremal Representations with Deep Archetypal Analysis. - Sebastian Mathias Keller; Maxim Samarin; Fabricio Arend Torres; Mario Wieser; Volker Roth 001 (Int. J. Comput. Vis., 2021)

Self-Supervised Representation Learning for High-Content Screening. - Daniel Siegismund; Mario Wieser; Stephan Heyse; Stephan Steigele (MIDL, 2022)

Vision Transformers Show Improved Robustness in High-Content Image Analysis. - Mario Wieser; Daniel Siegismund; Stephan Heyse; Stephan Steigele (SDS, 2022)

Learning Channel Importance for High Content Imaging with Interpretable Deep Input Channel Mixing. - Daniel Siegismund; Mario Wieser; Stephan Heyse; Stephan Steigele (DAGM, 2023)

PCIM: Learning Pixel Attributions via Pixel-wise Channel Isolation Mixing in High Content Imaging. - Daniel Siegismund; Mario Wieser; Stephan Heyse; Stephan Steigele (CoRR, 2024)

Revisiting Deep Archetypal Analysis for Phenotype Discovery in High Content Imaging. - Mario Wieser; Daniel Siegismund; Stephan Steigele (WACV, 2025)