Julia E. Vogt

Publication Activity (10 Years)

Years Active: 2010-2024
Publications (10 Years): 71

Top Topics

Constrained Clustering

Biomedical Data

Pointwise Mutual Information

Machine Learning

Top Venues

Publications

Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
Benchmarking the Fairness of Image Upsampling Methods. CoRR (2024)
Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer
Benchmarking the Fairness of Image Upsampling Methods. FAccT (2024)
Thomas M. Sutter, Yang Meng, Norbert Fortin, Julia E. Vogt, Stephan Mandt
Unity by Diversity: Improved Representation Learning in Multimodal VAEs. CoRR (2024)
Ricards Marcinkevics, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt
Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? CoRR (2024)
Kacper Sokol, Julia E. Vogt
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. CoRR (2024)
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
Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. Medical Image Anal. 91 (2024)
Moritz Vandenhirtz, Florian Barkmann, Laura Manduchi, Julia E. Vogt, Valentina Boeva
scTree: Discovering Cellular Hierarchies in the Presence of Batch Effects in scRNA-seq Data. CoRR (2024)
Kacper Sokol, Julia E. Vogt
What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks. CHI Extended Abstracts (2024)
Alain Ryser, Thomas M. Sutter, Alexander Marx, Julia E. Vogt
Anomaly Detection by Context Contrasting. CoRR (2024)
Jorge da Silva Goncalves, Laura Manduchi, Moritz Vandenhirtz, Julia E. Vogt
Structured Generations: Using Hierarchical Clusters to guide Diffusion Models. CoRR (2024)
Moritz Vandenhirtz, Sonia Laguna, Ricards Marcinkevics, Julia E. Vogt
Stochastic Concept Bottleneck Models. CoRR (2024)
Laura Manduchi, Kushagra Pandey, Robert Bamler, Ryan Cotterell, Sina Däubener, Sophie Fellenz, Asja Fischer, Thomas Gärtner, Matthias Kirchler, Marius Kloft, Yingzhen Li, Christoph Lippert, Gerard de Melo, Eric T. Nalisnick, Björn Ommer, Rajesh Ranganath, Maja Rudolph, Karen Ullrich, Guy Van den Broeck, Julia E. Vogt, Yixin Wang, Florian Wenzel, Frank Wood, Stephan Mandt, Vincent Fortuin
On the Challenges and Opportunities in Generative AI. CoRR (2024)
Hanna Ragnarsdóttir, Ece Ozkan, Holger Michel, Kieran Chin-Cheong, Laura Manduchi, Sven Wellmann, Julia E. Vogt
Deep Learning Based Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. Int. J. Comput. Vis. 132 (7) (2024)
Emanuele Palumbo, Laura Manduchi, Sonia Laguna, Daphné Chopard, Julia E. Vogt
Deep Generative Clustering with Multimodal Diffusion Variational Autoencoders. ICLR (2024)
Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, Julia E. Vogt
Tree Variational Autoencoders. NeurIPS (2023)
Ricards Marcinkevics, Julia E. Vogt
Interpretable and explainable machine learning: A methods-centric overview with concrete examples. WIREs Data. Mining. Knowl. Discov. 13 (3) (2023)
Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt
This Reads Like That: Deep Learning for Interpretable Natural Language Processing. EMNLP (2023)
Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
Identifiability Results for Multimodal Contrastive Learning. ICLR (2023)
Ricards Marcinkevics, Patricia Reis Wolfertstetter, Ugne Klimiene, Ece Ozkan, Kieran Chin-Cheong, Alyssia Paschke, Julia Zerres, Markus Denzinger, David Niederberger, Sven Wellmann, Christian Knorr, Julia E. Vogt
Interpretable and Intervenable Ultrasonography-based Machine Learning Models for Pediatric Appendicitis. CoRR (2023)
Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
Effective Bayesian Heteroscedastic Regression with Deep Neural Networks. NeurIPS (2023)
Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt
This Reads Like That: Deep Learning for Interpretable Natural Language Processing. CoRR (2023)
Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx
Beyond Normal: On the Evaluation of Mutual Information Estimators. CoRR (2023)
Emanuele Palumbo, Imant Daunhawer, Julia E. Vogt
MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises. ICLR (2023)
Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt
Differentiable Random Partition Models. NeurIPS (2023)
Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt
Identifiability Results for Multimodal Contrastive Learning. CoRR (2023)
Ece Ozkan, Thomas M. Sutter, Yurong Hu, Sebastian Balzer, Julia E. Vogt
M(otion)-Mode Based Prediction of Ejection Fraction Using Echocardiograms. DAGM (2023)
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML (2023)
Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx
Beyond Normal: On the Evaluation of Mutual Information Estimators. NeurIPS (2023)
Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal
How robust is unsupervised representation learning to distribution shift? ICLR (2023)
Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx
The Mixtures and the Neural Critics: On the Pointwise Mutual Information Profiles of Fine Distributions. CoRR (2023)
Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, Julia E. Vogt
Tree Variational Autoencoders. CoRR (2023)
Zixuan Xiao, Michal Muszynski, Ricards Marcinkevics, Lukas Zimmerli, Adam Daniel Ivankay, Dario Kohlbrenner, Manuel Kuhn, Yves Nordmann, Ulrich Muehlner, Christian Clarenbach, Julia E. Vogt, Thomas Brunschwiler
Breathing New Life into COPD Assessment: Multisensory Home-monitoring for Predicting Severity. ICMI (2023)
Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt
Learning Group Importance using the Differentiable Hypergeometric Distribution. ICLR (2023)
Moritz Vandenhirtz, Laura Manduchi, Ricards Marcinkevics, Julia E. Vogt
Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss. CoRR (2023)
Ece Ozkan, Thomas M. Sutter, Yurong Hu, Sebastian Balzer, Julia E. Vogt
M(otion)-mode Based Prediction of Ejection Fraction using Echocardiograms. CoRR (2023)
Kacper Sokol, Julia E. Vogt
(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability. CoRR (2023)
Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt
Differentiable Random Partition Models. CoRR (2023)
Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt
On the Limitations of Multimodal VAEs. ICLR (2022)
Hanna Ragnarsdóttir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan, Julia E. Vogt
Interpretable Prediction of Pulmonary Hypertension in Newborns using Echocardiograms. CoRR (2022)
Hanna Ragnarsdóttir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan, Julia E. Vogt
Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms. GCPR (2022)
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
A Deep Variational Approach to Clustering Survival Data. ICLR (2022)
Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx
On the Identifiability and Estimation of Causal Location-Scale Noise Models. CoRR (2022)
Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. MLHC (2022)
Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt
Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution. CoRR (2022)
Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge. CoRR (2022)
Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal
How robust are pre-trained models to distribution shift? CoRR (2022)
Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. CoRR (2022)
Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt
Interpretable Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models. CoRR (2022)
Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt
Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models. MLHC (2022)
Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt
On the Limitations of Multimodal VAEs. CoRR (2021)
Alexander H. Hatteland, Ricards Marcinkevics, Renaud Marquis, Thomas Frick, Ilona Hubbard, Julia E. Vogt, Thomas Brunschwiler, Philippe Ryvlin
Exploring Relationships between Cerebral and Peripheral Biosignals with Neural Networks. ICDH (2021)
Laura Manduchi, Ricards Marcinkevics, Michela Carlotta Massi, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Julia E. Vogt
A Deep Variational Approach to Clustering Survival Data. CoRR (2021)
Laura Manduchi, Matthias Hüser, Martin Faltys, Julia E. Vogt, Gunnar Rätsch, Vincent Fortuin
T-DPSOM: an interpretable clustering method for unsupervised learning of patient health states. CHIL (2021)
Ricards Marcinkevics, Julia E. Vogt
Interpretable Models for Granger Causality Using Self-explaining Neural Networks. CoRR (2021)
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Generalized Multimodal ELBO. ICLR (2021)
Juan M. Montoya, Imant Daunhawer, Julia E. Vogt, Marco A. Wiering
Decoupling State Representation Methods from Reinforcement Learning in Car Racing. ICAART (2) (2021)
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Generalized Multimodal ELBO. CoRR (2021)
Pedro Roig Aparicio, Ricards Marcinkevics, Patricia Reis Wolfertstetter, Sven Wellmann, Christian Knorr, Julia E. Vogt
Learning Medical Risk Scores for Pediatric Appendicitis. ICMLA (2021)
Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt
Deep Conditional Gaussian Mixture Model for Constrained Clustering. NeurIPS (2021)
Ricards Marcinkevics, Julia E. Vogt
Interpretable Models for Granger Causality Using Self-explaining Neural Networks. ICLR (2021)
Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt
Deep Conditional Gaussian Mixture Model for Constrained Clustering. CoRR (2021)
Thomas M. Sutter, Jan A. Roth, Kieran Chin-Cheong, Balthasar L. Hug, Julia E. Vogt
A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions. J. Am. Medical Informatics Assoc. 28 (4) (2021)
Kieran Chin-Cheong, Thomas M. Sutter, Julia E. Vogt
Generation of Differentially Private Heterogeneous Electronic Health Records. CoRR (2020)
Ricards Marcinkevics, Julia E. Vogt
Interpretability and Explainability: A Machine Learning Zoo Mini-tour. CoRR (2020)
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. CoRR (2020)
Varaha Karthik Pattisapu, Imant Daunhawer, Thomas J. Weikert, Alexander Sauter, Bram Stieltjes, Julia E. Vogt
PET-Guided Attention Network for Segmentation of Lung Tumors from PET/CT Images. GCPR (2020)
Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt
Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models. GCPR (2020)
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. NeurIPS (2020)
Stefan G. Stark, Stephanie L. Hyland, Melanie Fernandes Pradier, Kjong Lehmann, Andreas Wicki, Fernando Pérez-Cruz, Julia E. Vogt, Gunnar Rätsch
Unsupervised Extraction of Phenotypes from Cancer Clinical Notes for Association Studies. CoRR (2019)
Sandhya Prabhakaran, Julia E. Vogt
Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps. AIME (2019)
Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt
Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence. ViGIL@NeurIPS (2019)
Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
Probabilistic clustering of time-evolving distance data. Mach. Learn. 100 (2-3) (2015)
Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch
Probabilistic Clustering of Time-Evolving Distance Data. CoRR (2015)
Julia E. Vogt
Unsupervised Structure Detection in Biomedical Data. IEEE ACM Trans. Comput. Biol. Bioinform. 12 (4) (2015)
Julia E. Vogt, Volker Roth
A Complete Analysis of the l_1,p Group-Lasso CoRR (2012)
Julia E. Vogt, Volker Roth
A Complete Analysis of the l_1, p Group-Lasso. ICML (2012)
Sandhya Prabhakaran, Sudhir Raman, Julia E. Vogt, Volker Roth
Automatic Model Selection in Archetype Analysis. DAGM/OAGM Symposium (2012)
Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth
The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data. ICML (2010)
Julia E. Vogt, Volker Roth
Regularization. DAGM-Symposium (2010)