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ORCIDManuel Schürch
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2020 – today
- 2024
[c2]Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, Michael Krauthammer:
Simple Contrastive Representation Learning for Time Series Forecasting. ICASSP 2024: 6005-6009- [i11]Manuel Schürch
, Laura Boos, Viola Heinzelmann-Schwarz, Gabriele Gut, Michael Krauthammer, Andreas Wicki, Tumor Profiler Consortium:
Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data. CoRR abs/2402.12190 (2024) - [i10]Xiaochen Zheng, Manuel Schürch, Xingyu Chen, Maria Angeliki Komninou, Reto Schüpbach, Ahmed Allam, Jan Bartussek, Michael Krauthammer:
Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes. CoRR abs/2405.03327 (2024) - [i9]Cécile Trottet, Manuel Schürch, Ahmed Allam, Imon Barua, Liubov Petelytska, Oliver Distler, Anna-Maria Hoffmann-Vold, Michael Krauthammer, EUSTAR collaborators:
Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis. CoRR abs/2407.11427 (2024) - [i8]Michael Vollenweider, Manuel Schürch, Chiara Rohrer, Gabriele Gut, Michael Krauthammer, Andreas Wicki:
Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification. CoRR abs/2410.00509 (2024) - 2023
- [j2]Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon:
Correlated product of experts for sparse Gaussian process regression. Mach. Learn. 112(5): 1411-1432 (2023) - [i7]Xiaochen Zheng, Xingyu Chen, Manuel Schürch, Amina Mollaysa, Ahmed Allam, Michael Krauthammer:
SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting. CoRR abs/2303.18205 (2023) - [i6]Manuel Schürch, Xiang Li, Ahmed Allam, Giulia Rathmes, Amina Mollaysa, Claudia Cavelti-Weder, Michael Krauthammer:
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models. CoRR abs/2309.16521 (2023) - [i5]Xingyu Chen, Xiaochen Zheng, Amina Mollaysa, Manuel Schürch, Ahmed Allam, Michael Krauthammer:
Dynamic Local Attention with Hierarchical Patching for Irregular Clinical Time Series. CoRR abs/2311.07744 (2023) - [i4]Cécile Trottet, Manuel Schürch, Ahmed Allam, Imon Barua, Liubov Petelytska, Oliver Distler, Anna-Maria Hoffmann-Vold, Michael Krauthammer, EUSTAR collaborators:
Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes. CoRR abs/2311.08149 (2023) - 2021
- [c1]Lucas Kania, Manuel Schürch, Dario Azzimonti, Alessio Benavoli:
Sparse Information Filter for Fast Gaussian Process Regression. ECML/PKDD (3) 2021: 527-542 - [i3]Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon:
Correlated Product of Experts for Sparse Gaussian Process Regression. CoRR abs/2112.09519 (2021) - 2020
- [j1]Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon:
Recursive estimation for sparse Gaussian process regression. Autom. 120: 109127 (2020) - [i2]Dario Azzimonti, Manuel Schürch, Alessio Benavoli, Marco Zaffalon:
Orthogonally Decoupled Variational Fourier Features. CoRR abs/2007.06363 (2020)
2010 – 2019
- 2019
- [i1]Manuel Schürch, Dario Azzimonti, Alessio Benavoli, Marco Zaffalon:
Recursive Estimation for Sparse Gaussian Process Regression. CoRR abs/1905.11711 (2019)
Coauthor Index
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