What Does Evaluation of Explainable Artificial Intelligence Actually Tell Us? A Case for Compositional and Contextual Validation of XAI Building Blocks.

[BibT_eX]

[DOI]

Kacper Sokol

Julia E. Vogt

Proceedings of the Extended Abstracts of the CHI Conference on Human Factors in Computing Systems, 2024

2023

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

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

CoRR, 2023

(Un)reasonable Allure of Ante-hoc Interpretability for High-stakes Domains: Transparency Is Necessary but Insufficient for Explainability.

[BibT_eX]

[DOI]

Kacper Sokol

Julia E. Vogt

CoRR, 2023

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

[BibT_eX]

[DOI]

CoRR, 2023

Differentiable Random Partition Models.

[BibT_eX]

[DOI]

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

Tree Variational Autoencoders.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Alexander Immer

Christoph Schultheiss

Proceedings of the International Conference on Machine Learning, 2023

Breathing New Life into COPD Assessment: Multisensory Home-monitoring for Predicting Severity.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

How robust is unsupervised representation learning to distribution shift?

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

[BibT_eX]

[DOI]

Emanuele Palumbo

Imant Daunhawer

Julia E. Vogt

Proceedings of the Eleventh International Conference on Learning Representations, 2023

Identifiability Results for Multimodal Contrastive Learning.

[BibT_eX]

[DOI]

Proceedings of the Eleventh International Conference on Learning Representations, 2023

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

[BibT_eX]

[DOI]

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

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

[BibT_eX]

[DOI]

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

2022

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

[BibT_eX]

[DOI]

Ricards Marcinkevics

Ece Ozkan

Julia E. Vogt

CoRR, 2022

Interpretable Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models.

[BibT_eX]

[DOI]

CoRR, 2022

How robust are pre-trained models to distribution shift?

[BibT_eX]

[DOI]

CoRR, 2022

Continuous Relaxation For The Multivariate Non-Central Hypergeometric Distribution.

[BibT_eX]

[DOI]

CoRR, 2022

Anomaly Detection in Echocardiograms with Dynamic Variational Trajectory Models.

[BibT_eX]

[DOI]

Proceedings of the Machine Learning for Healthcare Conference, 2022

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

[BibT_eX]

[DOI]

Ricards Marcinkevics

Ece Ozkan

Julia E. Vogt

Proceedings of the Machine Learning for Healthcare Conference, 2022

A Deep Variational Approach to Clustering Survival Data.

[BibT_eX]

[DOI]

Laura Manduchi

Ricards Marcinkevics

Michela Carlotta Massi

Proceedings of the Tenth International Conference on Learning Representations, 2022

On the Limitations of Multimodal VAEs.

[BibT_eX]

[DOI]

Proceedings of the Tenth International Conference on Learning Representations, 2022

Interpretable Prediction of Pulmonary Hypertension in Newborns Using Echocardiograms.

[BibT_eX]

[DOI]

Proceedings of the Pattern Recognition, 2022

2021

A comparison of general and disease-specific machine learning models for the prediction of unplanned hospital readmissions.

[BibT_eX]

[DOI]

J. Am. Medical Informatics Assoc., 2021

A Deep Variational Approach to Clustering Survival Data.

[BibT_eX]

[DOI]

Laura Manduchi

Ricards Marcinkevics

Michela Carlotta Massi

CoRR, 2021

Deep Conditional Gaussian Mixture Model for Constrained Clustering.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Alexander H. Hatteland

Proceedings of the IEEE International Conference on Digital Health, 2021

Decoupling State Representation Methods from Reinforcement Learning in Car Racing.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

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

2020

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

[BibT_eX]

[DOI]

Ricards Marcinkevics

Julia E. Vogt

CoRR, 2020

Generation of Differentially Private Heterogeneous Electronic Health Records.

[BibT_eX]

[DOI]

Kieran Chin-Cheong

Thomas M. Sutter

Julia E. Vogt

CoRR, 2020

Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Varaha Karthik Pattisapu

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.

[BibT_eX]

[DOI]

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.

[BibT_eX]

[DOI]

Stefan G. Stark

Stephanie L. Hyland

Melanie Fernandes Pradier

CoRR, 2019

Bayesian Clustering for HIV1 Protease Inhibitor Contact Maps.

[BibT_eX]

[DOI]

Sandhya Prabhakaran

Julia E. Vogt

Proceedings of the Artificial Intelligence in Medicine, 2019

2015

Unsupervised Structure Detection in Biomedical Data.

[BibT_eX]

[DOI]

Julia E. Vogt

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

Probabilistic clustering of time-evolving distance data.

[BibT_eX]

[DOI]

Mach. Learn., 2015

2013

Structure Preserving Embedding of Dissimilarity Data.

[BibT_eX]

[DOI]

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

2012

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

[BibT_eX]

[DOI]

Julia E. Vogt

Volker Roth

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

Automatic Model Selection in Archetype Analysis.

[BibT_eX]

[DOI]

Proceedings of the Pattern Recognition, 2012

2010

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

[BibT_eX]

[DOI]

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

The Group-Lasso: l1, INFINITY Regularization versus l1, 2 Regularization.

[BibT_eX]

[DOI]

Julia E. Vogt

Volker Roth

Proceedings of the Pattern Recognition, 2010

Julia E. Vogt

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