Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm. - Ricards Marcinkevics; Steven Kelk; Carlo Galuzzi; Berthold Stegemann (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)

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

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)

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

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

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)

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

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

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

Explore, Support, and Interact: Scaling Interpretable and Explainable Machine Learning up to Realities of Biomedical Data. - Ricards Marcinkevics (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)