| Interferon-induced gene expression is a stronger predictor of treatment response than IL28B genotype in patients with hepatitis C MT Dill, FHT Duong, JE Vogt, S Bibert, PY Bochud, L Terracciano, ... Gastroenterology 140 (3), 1021-1031. e10, 2011 | 295 | 2011 |
| Interpretability and explainability: A machine learning zoo mini-tour R Marcinkevičs, JE Vogt arXiv preprint arXiv:2012.01805, 2020 | 187 | 2020 |
| Generalized Multimodal ELBO TM Sutter, I Daunhawer, JE Vogt The International Conference on Learning Representations (ICLR), 2021 | 102 | 2021 |
| Introduction to Machine Learning in Digital Healthcare Epidemiology MD Jan A. Roth, MD Manuel Battegay, MD Fabrice Juchler, ... Infection Control & Hospital Epidemiology, 2018 | 100 | 2018 |
| Interpretable and explainable machine learning: a methods‐centric overview with concrete examples R Marcinkevičs, JE Vogt Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 13 (3 …, 2023 | 93 | 2023 |
| Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence T Sutter, I Daunhawer, JE Vogt Neural Information Processing Systems (NeurIPS) 2020, 2020 | 86 | 2020 |
| Gene expression analysis of biopsy samples reveals critical limitations of transcriptome‐based molecular classifications of hepatocellular carcinoma Z Makowska, T Boldanova, D Adametz, L Quagliata, JE Vogt, MT Dill, ... The Journal of Pathology: Clinical Research 2 (2), 80-92, 2016 | 78 | 2016 |
| Re-focusing explainability in medicine L Arbelaez Ossa, G Starke, G Lorenzini, JE Vogt, DM Shaw, BS Elger Digital health 8, 20552076221074488, 2022 | 76 | 2022 |
| Interpretable Models for Granger Causality Using Self-explaining Neural Networks R Marcinkevičs, JE Vogt The International Conference on Learning Representations (ICLR), 2021 | 72 | 2021 |
| Pegylated IFN-α regulates hepatic gene expression through transient Jak/STAT activation MT Dill, Z Makowska, G Trincucci, AJ Gruber, JE Vogt, M Filipowicz, ... The Journal of clinical investigation 124 (4), 1568-1581, 2014 | 61 | 2014 |
| Pharmacometrics and machine learning partner to advance clinical data analysis G Koch, M Pfister, I Daunhawer, M Wilbaux, S Wellmann, JE Vogt Clinical Pharmacology & Therapeutics 107 (4), 926-933, 2020 | 60 | 2020 |
| Using Machine Learning to Predict the Diagnosis, Management and Severity of Pediatric Appendicitis R Marcinkevics, P Reis Wolfertstetter, S Wellmann, C Knorr, JE Vogt Frontiers in Pediatrics 9, 360, 2021 | 55 | 2021 |
| Enhanced early prediction of clinically relevant neonatal hyperbilirubinemia with machine learning I Daunhawer, S Kasser, G Koch, L Sieber, H Cakal, J Tütsch, M Pfister, ... Pediatric research 86 (1), 122-127, 2019 | 55 | 2019 |
| A Deep Variational Approach to Clustering Survival Data L Manduchi, R Marcinkevics, MC Massi, V Gotta, T Müller, F Vasella, ... The Eleventh International Conference on Learning Representations (ICLR) 2022, 2022 | 48 | 2022 |
| On the identifiability and estimation of causal location-scale noise models A Immer, C Schultheiss, JE Vogt, B Schölkopf, P Bühlmann, A Marx International Conference on Machine Learning (ICML) 2023, 14316-14332, 2023 | 41 | 2023 |
| A complete analysis of the l_1, p group-lasso J Vogt, V Roth International Conference of Machine Learning (ICML), 2012 | 41 | 2012 |
| On the limitations of multimodal VAEs I Daunhawer, TM Sutter, K Chin-Cheong, E Palumbo, JE Vogt The Eleventh International Conference on Learning Representations (ICLR) 2022, 2022 | 39 | 2022 |
| Generation of Heterogeneous Synthetic Electronic Health Records using GANs K Chin-Cheong, T Sutter, JE Vogt Machine Learning for Health Workshop, NeurIPS 2019, Vancouver, Canada, 2019 | 39 | 2019 |
| Beyond the randomized clinical trial: innovative data science to close the pediatric evidence gap SC Goulooze, LB Zwep, JE Vogt, EHJ Krekels, T Hankemeier, ... Clinical Pharmacology & Therapeutics 107 (4), 786-795, 2020 | 38 | 2020 |
| Identifiability results for multimodal contrastive learning I Daunhawer, A Bizeul, E Palumbo, A Marx, JE Vogt International Conference on Learning Representation (ICLR), 2023 | 35 | 2023 |
Thomas M. SutterPostdoc, ETH ZurichEmail verificata su inf.ethz.ch