The Group-Lasso: l1, INFINITY Regularization versus l1, 2 Regularization. - Julia E. Vogt; Volker Roth 001 (DAGM-Symposium, 2010)

The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data. - Julia E. Vogt; Sandhya Prabhakaran; Thomas J. Fuchs; Volker Roth 001 (ICML, 2010)

Automatic Model Selection in Archetype Analysis. - Sandhya Prabhakaran; Sudhir Raman; Julia E. Vogt; Volker Roth 001 (DAGM/OAGM Symposium, 2012)

A Complete Analysis of the l_1, p Group-Lasso. - Julia E. Vogt; Volker Roth 001 (ICML, 2012)

Structure Preserving Embedding of Dissimilarity Data. - Volker Roth 001; Thomas J. Fuchs; Julia E. Vogt; Sandhya Prabhakaran; Joachim M. Buhmann (Similarity-Based Pattern Analysis and Recognition, 2013)

Probabilistic clustering of time-evolving distance data. - Julia E. Vogt; Marius Kloft; Stefan Stark; Sudhir Raman; Sandhya Prabhakaran; Volker Roth 001; Gunnar Rätsch (Mach. Learn., 2015)

Unsupervised Structure Detection in Biomedical Data. - Julia E. Vogt (IEEE ACM Trans. Comput. Biol. Bioinform., 2015)

Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps. - Sandhya Prabhakaran; Julia E. Vogt (AIME, 2019)

Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies. - Stefan G. Stark; Stephanie L. Hyland; Melanie Fernandes Pradier; Kjong-Van Lehmann; Andreas Wicki; Fernando Pérez-Cruz; Julia E. Vogt; Gunnar Rätsch (CoRR, 2019)

Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models. - Imant Daunhawer; Thomas M. Sutter; Ricards Marcinkevics; Julia E. Vogt (GCPR, 2020)

PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images. - Varaha Karthik Pattisapu; Imant Daunhawer; Thomas J. Weikert; Alexander Sauter; Bram Stieltjes; Julia E. Vogt (GCPR, 2020)

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. - Thomas M. Sutter; Imant Daunhawer; Julia E. Vogt (NeurIPS, 2020)

Generation of Differentially Private Heterogeneous Electronic Health Records. - Kieran Chin-Cheong; Thomas M. Sutter; Julia E. Vogt (CoRR, 2020)

Interpretability and Explainability: A Machine Learning Zoo Mini-tour. - Ricards Marcinkevics; Julia E. Vogt (CoRR, 2020)

T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. - Laura Manduchi; Matthias Hüser; Martin Faltys; Julia E. Vogt; Gunnar Rätsch; Vincent Fortuin (CHIL, 2021)

Decoupling State Representation Methods from Reinforcement Learning in Car Racing. - Juan M. Montoya; Imant Daunhawer; Julia E. Vogt; Marco A. Wiering (ICAART (2), 2021)

Exploring Relationships between Cerebral and Peripheral Biosignals with Neural Networks. - Alexander H. Hatteland; Ricards Marcinkevics; Renaud Marquis; Thomas Frick; Ilona Hubbard; Julia E. Vogt; Thomas Brunschwiler; Philippe Ryvlin (ICDH, 2021)

Interpretable Models for Granger Causality Using Self-explaining Neural Networks. - Ricards Marcinkevics; Julia E. Vogt (ICLR, 2021)

Generalized Multimodal ELBO. - Thomas M. Sutter; Imant Daunhawer; Julia E. Vogt (ICLR, 2021)

Learning Medical Risk Scores for Pediatric Appendicitis. - Pedro Roig Aparicio; Ricards Marcinkevics; Patricia Reis Wolfertstetter; Sven Wellmann; Christian Knorr; Julia E. Vogt (ICMLA, 2021)

Deep Conditional Gaussian Mixture Model for Constrained Clustering. - Laura Manduchi; Kieran Chin-Cheong; Holger Michel; Sven Wellmann; Julia E. Vogt (NeurIPS, 2021)

A Deep Variational Approach to Clustering Survival Data. - Laura Manduchi; Ricards Marcinkevics; Michela Carlotta Massi; Verena Gotta; Timothy Müller; Flavio Vasella; Marian C. Neidert; Marc Pfister; Julia E. Vogt (CoRR, 2021)

A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions. - Thomas M. Sutter; Jan A. Roth; Kieran Chin-Cheong; Balthasar L. Hug; Julia E. Vogt (J. Am. Medical Informatics Assoc., 2021)

Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. - Hanna Ragnarsdóttir; Laura Manduchi; Holger Michel; Fabian Laumer; Sven Wellmann; Ece Ozkan; Julia E. Vogt (GCPR, 2022)

On the Limitations of Multimodal VAEs. - Imant Daunhawer; Thomas M. Sutter; Kieran Chin-Cheong; Emanuele Palumbo; Julia E. Vogt (ICLR, 2022)

A Deep Variational Approach to Clustering Survival Data. - Laura Manduchi; Ricards Marcinkevics; Michela Carlotta Massi; Thomas J. Weikert; Alexander Sauter; Verena Gotta; Timothy Müller; Flavio Vasella; Marian C. Neidert; Marc Pfister; Bram Stieltjes; Julia E. Vogt (ICLR, 2022)

Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. - Ricards Marcinkevics; Ece Ozkan; Julia E. Vogt (MLHC, 2022)

Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models. - Alain Ryser; Laura Manduchi; Fabian Laumer; Holger Michel; Sven Wellmann; Julia E. Vogt (MLHC, 2022)

Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution. - Thomas M. Sutter; Laura Manduchi; Alain Ryser; Julia E. Vogt (CoRR, 2022)

How robust are pre-trained models to distribution shift? - Yuge Shi; Imant Daunhawer; Julia E. Vogt; Philip H. S. Torr; Amartya Sanyal (CoRR, 2022)

Interpretable Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models. - Alain Ryser; Laura Manduchi; Fabian Laumer; Holger Michel; Sven Wellmann; Julia E. Vogt (CoRR, 2022)

Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge. - Ricards Marcinkevics; Ece Ozkan; Julia E. Vogt (CoRR, 2022)

M(otion)-Mode Based Prediction of Ejection Fraction Using Echocardiograms. - Ece Ozkan; Thomas M. Sutter; Yurong Hu; Sebastian Balzer; Julia E. Vogt (DAGM, 2023)

This Reads Like That: Deep Learning for Interpretable Natural Language Processing. - Claudio Fanconi; Moritz Vandenhirtz; Severin Husmann; Julia E. Vogt (EMNLP, 2023)

Identifiability Results for Multimodal Contrastive Learning. - Imant Daunhawer; Alice Bizeul; Emanuele Palumbo; Alexander Marx 001; Julia E. Vogt (ICLR, 2023)

MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises. - Emanuele Palumbo; Imant Daunhawer; Julia E. Vogt (ICLR, 2023)

How robust is unsupervised representation learning to distribution shift? - Yuge Shi; Imant Daunhawer; Julia E. Vogt; Philip H. S. Torr; Amartya Sanyal (ICLR, 2023)

Learning Group Importance using the Differentiable Hypergeometric Distribution. - Thomas M. Sutter; Laura Manduchi; Alain Ryser; Julia E. Vogt (ICLR, 2023)

Breathing New Life into COPD Assessment: Multisensory Home-monitoring for Predicting Severity. - Zixuan Xiao; Michal Muszynski; Ricards Marcinkevics; Lukas Zimmerli; Adam Daniel Ivankay; Dario Kohlbrenner; Manuel Kuhn; Yves Nordmann; Ulrich Muehlner; Christian F. Clarenbach; Julia E. Vogt; Thomas Brunschwiler (ICMI, 2023)

On the Identifiability and Estimation of Causal Location-Scale Noise Models. - Alexander Immer; Christoph Schultheiss; Julia E. Vogt; Bernhard Schölkopf; Peter Bühlmann; Alexander Marx 001 (ICML, 2023)

Beyond Normal: On the Evaluation of Mutual Information Estimators. - Pawel Czyz; Frederic Grabowski; Julia E. Vogt; Niko Beerenwinkel; Alexander Marx 001 (NeurIPS, 2023)

Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. - Alexander Immer; Emanuele Palumbo; Alexander Marx 001; Julia E. Vogt (NeurIPS, 2023)

Tree Variational Autoencoders. - Laura Manduchi; Moritz Vandenhirtz; Alain Ryser; Julia E. Vogt (NeurIPS, 2023)

Differentiable Random Partition Models. - Thomas M. Sutter; Alain Ryser; Joram Liebeskind; Julia E. Vogt (NeurIPS, 2023)

Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss. - Moritz Vandenhirtz; Laura Manduchi; Ricards Marcinkevics; Julia E. Vogt (CoRR, 2023)

(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability. - Kacper Sokol; Julia E. Vogt (CoRR, 2023)

The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions. - Pawel Czyz; Frederic Grabowski; Julia E. Vogt; Niko Beerenwinkel; Alexander Marx 001 (CoRR, 2023)

Interpretable and explainable machine learning: A methods-centric overview with concrete examples. - Ricards Marcinkevics; Julia E. Vogt (WIREs Data. Mining. Knowl. Discov., 2023)

What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. - Kacper Sokol; Julia E. Vogt (CHI Extended Abstracts, 2024)

Can Generative AI Learn Physiological Waveform Morphologies? A Study on Denoising Intracardiac Signals in Ischemic Cardiomyopathy. - Samuel Ruipérez-Campillo; Alain Ryser; Thomas M. Sutter; Ruibin Feng; Prasanth Ganesan; Brototo Deb; Kelly A. Brennan; Maarten Z. Kolk; Fleur V. Y. Tjong; Albert J. Rogers; Sanjiv M. Narayan; Julia E. Vogt (EMBC, 2024)

Benchmarking the Fairness of Image Upsampling Methods. - Mike Laszkiewicz; Imant Daunhawer; Julia E. Vogt; Asja Fischer; Johannes Lederer (FAccT, 2024)

Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders. - Emanuele Palumbo; Laura Manduchi; Sonia Laguna; Daphné Chopard; Julia E. Vogt (ICLR, 2024)

Transfer Learning for Pediatric Glucose Forecasting. - Alain Ryser; Chuhao Feng; Tobias Scheithauer; Marc Pfister; Marie-Anne Burckhardt; Sara Bachmann; Alexander Marx 001; Julia E. Vogt (ML4H@NeurIPS, 2024)

Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? - Sonia Laguna; Ricards Marcinkevics; Moritz Vandenhirtz; Julia E. Vogt (NeurIPS, 2024)

Unity by Diversity: Improved Representation Learning for Multimodal VAEs. - Thomas M. Sutter; Yang Meng; Andrea Agostini; Daphné Chopard; Norbert Fortin; Julia E. Vogt; Babak Shahbaba; Stephan Mandt (NeurIPS, 2024)

Stochastic Concept Bottleneck Models. - Moritz Vandenhirtz; Sonia Laguna; Ricards Marcinkevics; Julia E. Vogt (NeurIPS, 2024)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; Christoph Lippert; et al. (CoRR, 2024)

Unity by Diversity: Improved Representation Learning in Multimodal VAEs. - Thomas M. Sutter; Yang Meng; Norbert Fortin; Julia E. Vogt; Stephan Mandt (CoRR, 2024)

Anomaly Detection by Context Contrasting. - Alain Ryser; Thomas M. Sutter; Alexander Marx 001; Julia E. Vogt (CoRR, 2024)

scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data. - Moritz Vandenhirtz; Florian Barkmann; Laura Manduchi; Julia E. Vogt; Valentina Boeva (CoRR, 2024)

Structured Generations: Using Hierarchical Clusters to guide Diffusion Models. - Jorge da Silva Goncalves; Laura Manduchi; Moritz Vandenhirtz; Julia E. Vogt (CoRR, 2024)

From Logits to Hierarchies: Hierarchical Clustering made Simple. - Emanuele Palumbo; Moritz Vandenhirtz; Alain Ryser; Imant Daunhawer; Julia E. Vogt (CoRR, 2024)

Hierarchical Clustering for Conditional Diffusion in Image Generation. - Jorge da Silva Goncalves; Laura Manduchi; Moritz Vandenhirtz; Julia E. Vogt (CoRR, 2024)

Exploiting Interpretable Capabilities with Concept-Enhanced Diffusion and Prototype Networks. - Alba Carballo-Castro; Sonia Laguna; Moritz Vandenhirtz; Julia E. Vogt (CoRR, 2024)

Automatic Classification of General Movements in Newborns. - Daphné Chopard; Sonia Laguna; Kieran Chin-Cheong; Annika Dietz; Anna Badura; Sven Wellmann; Julia E. Vogt (CoRR, 2024)

Weakly-Supervised Multimodal Learning on MIMIC-CXR. - Andrea Agostini; Daphné Chopard; Yang Meng; Norbert Fortin; Babak Shahbaba; Stephan Mandt; Thomas M. Sutter; Julia E. Vogt (CoRR, 2024)

Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. - Ricards Marcinkevics; Patricia Reis Wolfertstetter; Ugne Klimiene; Kieran Chin-Cheong; Alyssia Paschke; Julia Zerres; Markus Denzinger; David Niederberger; Sven Wellmann; Ece Ozkan; Christian Knorr; Julia E. Vogt (Medical Image Anal., 2024)

Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. - Hanna Ragnarsdóttir; Ece Ozkan; Holger Michel; Kieran Chin-Cheong; Laura Manduchi; Sven Wellmann; Julia E. Vogt (Int. J. Comput. Vis., 2024)

Cross-Entropy Is All You Need To Invert the Data Generating Process. - Patrik Reizinger; Alice Bizeul; Attila Juhos; Julia E. Vogt; Randall Balestriero; Wieland Brendel; David A. Klindt (ICLR, 2025)

AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation. - Shalmali Joshi; Iñigo Urteaga; Wouter A. C. van Amsterdam; George Hripcsak; Pierre A. Elias; Benjamin R. C. Amor; Noémie Elhadad; James C. Fackler; Mark P. Sendak; Jenna Wiens; Kaivalya Deshpande; Yoav Wald; et al. (J. Am. Medical Informatics Assoc., 2025)

Artificial intelligence should genuinely support clinical reasoning and decision making to bridge the translational gap. - Kacper Sokol; James C. Fackler; Julia E. Vogt (npj Digit. Medicine, 2025)

On the Properties and Estimation of Pointwise Mutual Information Profiles. - Pawel Czyz; Frederic Grabowski; Julia E. Vogt; Niko Beerenwinkel; Alexander Marx 001 (Trans. Mach. Learn. Res., 2025)

On the Challenges and Opportunities in Generative AI. - Laura Manduchi; Clara Meister; Kushagra Pandey; Robert Bamler; Ryan Cotterell; Sina Däubener; Sophie Fellenz; Asja Fischer; Thomas Gärtner 004; Matthias Kirchler; Marius Kloft; Yingzhen Li; et al. (Trans. Mach. Learn. Res., 2025)

RadVLM: A Multitask Conversational Vision-Language Model for Radiology. - Nicolas Deperrois; Hidetoshi Matsuo; Samuel Ruipérez-Campillo; Moritz Vandenhirtz; Sonia Laguna; Alain Ryser; Koji Fujimoto; Mizuho Nishio; Thomas M. Sutter; Julia E. Vogt; Jonas Kluckert; Thomas Frauenfelder; et al. (CoRR, 2025)

From Pixels to Components: Eigenvector Masking for Visual Representation Learning. - Alice Bizeul; Thomas M. Sutter; Alain Ryser; Bernhard Schölkopf; Julius von Kügelgen; Julia E. Vogt (CoRR, 2025)

Revisiting Automatic Data Curation for Vision Foundation Models in Digital Pathology. - Boqi Chen; Cédric Vincent-Cuaz; Lydia A. Schoenpflug; Manuel Madeira; Lisa Fournier; Vaishnavi Subramanian; Sonali Andani; Samuel Ruipérez-Campillo; Julia E. Vogt; Raphaëlle Luisier; Dorina Thanou; Viktor H. Koelzer; et al. (CoRR, 2025)

Beyond Glucose-Only Assessment: Advancing Nocturnal Hypoglycemia Prediction in Children with Type 1 Diabetes. - Marco Voegeli; Sonia Laguna; Heike Leutheuser; Marc Pfister; Marie-Anne Burckhardt; Julia E. Vogt (CoRR, 2025)

Measuring Leakage in Concept-Based Methods: An Information Theoretic Approach. - Mikael Makonnen; Moritz Vandenhirtz; Sonia Laguna; Julia E. Vogt (CoRR, 2025)

From Pixels to Perception: Interpretable Predictions via Instance-wise Grouped Feature Selection. - Moritz Vandenhirtz; Julia E. Vogt (CoRR, 2025)

scSSL-Bench: Benchmarking Self-Supervised Learning for Single-Cell Data. - Olga Ovcharenko; Florian Barkmann; Philip Toma; Imant Daunhawer; Julia E. Vogt; Sebastian Schelter; Valentina Boeva (CoRR, 2025)

Leveraging the Structure of Medical Data for Improved Representation Learning. - Andrea Agostini; Sonia Laguna; Alain Ryser; Samuel Ruipérez-Campillo; Moritz Vandenhirtz; Nicolas Deperrois; Farhad Nooralahzadeh; Michael Krauthammer; Thomas M. Sutter; Julia E. Vogt (CoRR, 2025)

Interpretable Diffusion Models with B-cos Networks. - Nicola Bernold; Moritz Vandenhirtz; Alice Bizeul; Julia E. Vogt (CoRR, 2025)

Interpretable Reward Modeling with Active Concept Bottlenecks. - Sonia Laguna; Katarzyna Kobalczyk; Julia E. Vogt; Mihaela van der Schaar (CoRR, 2025)

Predicting Pulmonary Hypertension in Newborns: A Multi-view VAE Approach. - Lucas Erlacher; Samuel Ruipérez-Campillo; Holger Michel; Sven Wellmann; Thomas M. Sutter; Ece Ozkan; Julia E. Vogt (CoRR, 2025)

A Denoising VAE for Intracardiac Time Series in Ischemic Cardiomyopathy. - Samuel Ruipérez-Campillo; Alain Ryser; Thomas M. Sutter; Ruibin Feng; Prasanth Ganesan; Brototo Deb; Kelly A. Brennan; Maxime Pedron; Albert J. Rogers; Maarten Z. Kolk; Fleur V. Y. Tjong; Sanjiv M. Narayan; et al. (CoRR, 2025)