Julia E. Vogt

According to our database, Julia E. Vogt authored at least 54 papers between 2010 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

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., January, 2024

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

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

On the Challenges and Opportunities in Generative AI.

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

CoRR, 2024

Benchmarking the Fairness of Image Upsampling Methods.

Mike Laszkiewicz, Imant Daunhawer, Julia E. Vogt, Asja Fischer, Johannes Lederer

CoRR, 2024

Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable?

Ricards Marcinkevics, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt

CoRR, 2024

2023

Interpretable and explainable machine learning: A methods-centric overview with concrete examples.

Ricards Marcinkevics, Julia E. Vogt

WIREs Data. Mining. Knowl. Discov., 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

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

Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss.

Moritz Vandenhirtz, Laura Manduchi, Ricards Marcinkevics, Julia E. Vogt

CoRR, 2023

Differentiable Random Partition Models.

Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E. Vogt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Tree Variational Autoencoders.

Laura Manduchi, Moritz Vandenhirtz, Alain Ryser, Julia E. Vogt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Effective Bayesian Heteroscedastic Regression with Deep Neural Networks.

Alexander Immer, Emanuele Palumbo, Alexander Marx, Julia E. Vogt

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Beyond Normal: On the Evaluation of Mutual Information Estimators.

Pawel Czyz, Frederic Grabowski, Julia E. Vogt, Niko Beerenwinkel, Alexander Marx

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 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

Proceedings of the International Conference on Machine Learning, 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

Proceedings of the 25th International Conference on Multimodal Interaction, 2023

Learning Group Importance using the Differentiable Hypergeometric Distribution.

Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

How robust is unsupervised representation learning to distribution shift?

Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal

Proceedings of the Eleventh International Conference on Learning Representations, 2023

MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises.

Emanuele Palumbo, Imant Daunhawer, Julia E. Vogt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Identifiability Results for Multimodal Contrastive Learning.

Imant Daunhawer, Alice Bizeul, Emanuele Palumbo, Alexander Marx, Julia E. Vogt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

This Reads Like That: Deep Learning for Interpretable Natural Language Processing.

Claudio Fanconi, Moritz Vandenhirtz, Severin Husmann, Julia E. Vogt

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

M(otion)-Mode Based Prediction of Ejection Fraction Using Echocardiograms.

Ece Ozkan, Thomas M. Sutter, Yurong Hu, Sebastian Balzer, Julia E. Vogt

Proceedings of the Pattern Recognition - 45th DAGM German Conference, 2023

2022

Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge.

Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt

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

How robust are pre-trained models to distribution shift?

Yuge Shi, Imant Daunhawer, Julia E. Vogt, Philip H. S. Torr, Amartya Sanyal

CoRR, 2022

Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution.

Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E. Vogt

CoRR, 2022

Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models.

Alain Ryser, Laura Manduchi, Fabian Laumer, Holger Michel, Sven Wellmann, Julia E. Vogt

Proceedings of the Machine Learning for Healthcare Conference, 2022

Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods.

Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt

Proceedings of the Machine Learning for Healthcare Conference, 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

Proceedings of the Tenth International Conference on Learning Representations, 2022

On the Limitations of Multimodal VAEs.

Imant Daunhawer, Thomas M. Sutter, Kieran Chin-Cheong, Emanuele Palumbo, Julia E. Vogt

Proceedings of the Tenth International Conference on Learning Representations, 2022

Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms.

Hanna Ragnarsdóttir, Laura Manduchi, Holger Michel, Fabian Laumer, Sven Wellmann, Ece Ozkan, Julia E. Vogt

Proceedings of the Pattern Recognition, 2022

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

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

Deep Conditional Gaussian Mixture Model for Constrained Clustering.

Laura Manduchi, Kieran Chin-Cheong, Holger Michel, Sven Wellmann, Julia E. Vogt

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Learning Medical Risk Scores for Pediatric Appendicitis.

Pedro Roig Aparicio, Ricards Marcinkevics, Patricia Reis Wolfertstetter, Sven Wellmann, Christian Knorr, Julia E. Vogt

Proceedings of the 20th IEEE International Conference on Machine Learning and Applications, 2021

Generalized Multimodal ELBO.

Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt

Proceedings of the 9th International Conference on Learning Representations, 2021

Interpretable Models for Granger Causality Using Self-explaining Neural Networks.

Ricards Marcinkevics, Julia E. Vogt

Proceedings of the 9th International Conference on Learning Representations, 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

Proceedings of the IEEE International Conference on Digital Health, 2021

Decoupling State Representation Methods from Reinforcement Learning in Car Racing.

Juan M. Montoya, Imant Daunhawer, Julia E. Vogt, Marco A. Wiering

Proceedings of the 13th International Conference on Agents and Artificial Intelligence, 2021

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

Proceedings of the ACM CHIL '21: ACM Conference on Health, 2021

2020

Interpretability and Explainability: A Machine Learning Zoo Mini-tour.

Ricards Marcinkevics, Julia E. Vogt

CoRR, 2020

Generation of Differentially Private Heterogeneous Electronic Health Records.

Kieran Chin-Cheong, Thomas M. Sutter, Julia E. Vogt

CoRR, 2020

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence.

Thomas M. Sutter, Imant Daunhawer, Julia E. Vogt

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 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

Proceedings of the Pattern Recognition - 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28, 2020

Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models.

Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt

Proceedings of the Pattern Recognition - 42nd DAGM German Conference, DAGM GCPR 2020, Tübingen, Germany, September 28, 2020

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

Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps.

Sandhya Prabhakaran, Julia E. Vogt

Proceedings of the Artificial Intelligence in Medicine, 2019

2015

Unsupervised Structure Detection in Biomedical Data.

Julia E. Vogt

IEEE ACM Trans. Comput. Biol. Bioinform., 2015

Probabilistic clustering of time-evolving distance data.

Julia E. Vogt, Marius Kloft, Stefan Stark, Sudhir Raman, Sandhya Prabhakaran, Volker Roth, Gunnar Rätsch

Mach. Learn., 2015

2013

Structure Preserving Embedding of Dissimilarity Data.

Volker Roth, Thomas J. Fuchs, Julia E. Vogt, Sandhya Prabhakaran, Joachim M. Buhmann

Proceedings of the Similarity-Based Pattern Analysis and Recognition, 2013

2012

A Complete Analysis of the l_1, p Group-Lasso.

Julia E. Vogt, Volker Roth

Proceedings of the 29th International Conference on Machine Learning, 2012

Automatic Model Selection in Archetype Analysis.

Sandhya Prabhakaran, Sudhir Raman, Julia E. Vogt, Volker Roth

Proceedings of the Pattern Recognition, 2012

2010

The Translation-invariant Wishart-Dirichlet Process for Clustering Distance Data.

Julia E. Vogt, Sandhya Prabhakaran, Thomas J. Fuchs, Volker Roth

Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010

The Group-Lasso: <i>l</i>_{1, INFINITY} Regularization versus <i>l</i>_{1, 2} Regularization.

Julia E. Vogt, Volker Roth

Proceedings of the Pattern Recognition, 2010