Ricards Marcinkevics

22 papers A* 4B 1C 1Journal 12Unranked 3

Year	Rank	Type	Title / Venue / Authors
2024	A*	`conf`	Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? NeurIPS Sonia Laguna, Ricards Marcinkevics, Moritz Vandenhirtz, Julia E. Vogt
2024	J	`jnl`	Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? CoRR Ricards Marcinkevics, Sonia Laguna, Moritz Vandenhirtz, Julia E. Vogt
2024	—	`—`	Explore, Support, and Interact: Scaling Interpretable and Explainable Machine Learning up to Realities of Biomedical Data. Ricards Marcinkevics
2024	J	`jnl`	Interpretable and intervenable ultrasonography-based machine learning models for pediatric appendicitis. Medical Image Anal. 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
2024	A*	`conf`	Stochastic Concept Bottleneck Models. NeurIPS Moritz Vandenhirtz, Sonia Laguna, Ricards Marcinkevics, Julia E. Vogt
2024	J	`jnl`	Stochastic Concept Bottleneck Models. CoRR Moritz Vandenhirtz, Sonia Laguna, Ricards Marcinkevics, Julia E. Vogt
2023	B	`conf`	Breathing New Life into COPD Assessment: Multisensory Home-monitoring for Predicting Severity. ICMI 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
2023	J	`jnl`	Interpretable and Intervenable Ultrasonography-based Machine Learning Models for Pediatric Appendicitis. CoRR 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
2023	J	`jnl`	Interpretable and explainable machine learning: A methods-centric overview with concrete examples. WIREs Data. Mining. Knowl. Discov. Ricards Marcinkevics, Julia E. Vogt
2023	J	`jnl`	Signal Is Harder To Learn Than Bias: Debiasing with Focal Loss. CoRR Moritz Vandenhirtz, Laura Manduchi, Ricards Marcinkevics, Julia E. Vogt
2022	A*	`conf`	A Deep Variational Approach to Clustering Survival Data. ICLR 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
2022	—	`conf`	Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. MLHC Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
2022	J	`jnl`	Debiasing Deep Chest X-Ray Classifiers using Intra- and Post-processing Methods. CoRR Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
2022	J	`jnl`	Introduction to Machine Learning for Physicians: A Survival Guide for Data Deluge. CoRR Ricards Marcinkevics, Ece Ozkan, Julia E. Vogt
2021	J	`jnl`	A Deep Variational Approach to Clustering Survival Data. CoRR Laura Manduchi, Ricards Marcinkevics, Michela Carlotta Massi, Verena Gotta, Timothy Müller, Flavio Vasella, Marian C. Neidert, Marc Pfister, Julia E. Vogt
2021	—	`conf`	Exploring Relationships between Cerebral and Peripheral Biosignals with Neural Networks. ICDH Alexander H. Hatteland, Ricards Marcinkevics, Renaud Marquis, Thomas Frick, Ilona Hubbard, Julia E. Vogt, Thomas Brunschwiler, Philippe Ryvlin
2021	A*	`conf`	Interpretable Models for Granger Causality Using Self-explaining Neural Networks. ICLR Ricards Marcinkevics, Julia E. Vogt
2021	J	`jnl`	Interpretable Models for Granger Causality Using Self-explaining Neural Networks. CoRR Ricards Marcinkevics, Julia E. Vogt
2021	C	`conf`	Learning Medical Risk Scores for Pediatric Appendicitis. ICMLA Pedro Roig Aparicio, Ricards Marcinkevics, Patricia Reis Wolfertstetter, Sven Wellmann, Christian Knorr, Julia E. Vogt
2020	J	`jnl`	Interpretability and Explainability: A Machine Learning Zoo Mini-tour. CoRR Ricards Marcinkevics, Julia E. Vogt
2020	—	`conf`	Self-supervised Disentanglement of Modality-Specific and Shared Factors Improves Multimodal Generative Models. GCPR Imant Daunhawer, Thomas M. Sutter, Ricards Marcinkevics, Julia E. Vogt
2019	J	`jnl`	Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm. CoRR Ricards Marcinkevics, Steven Kelk, Carlo Galuzzi, Berthold Stegemann