Recursive estimation for sparse Gaussian process regression. - Manuel Schürch; Dario Azzimonti; Alessio Benavoli; Marco Zaffalon (Autom., 2020)

Orthogonally Decoupled Variational Fourier Features. - Dario Azzimonti; Manuel Schürch; Alessio Benavoli; Marco Zaffalon (CoRR, 2020)

Sparse Information Filter for Fast Gaussian Process Regression. - Lucas Kania; Manuel Schürch; Dario Azzimonti; Alessio Benavoli (ECML/PKDD (3), 2021)

SimTS: Rethinking Contrastive Representation Learning for Time Series Forecasting. - Xiaochen Zheng; Xingyu Chen; Manuel Schürch; Amina Mollaysa; Ahmed Allam; Michael Krauthammer (CoRR, 2023)

Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models. - Manuel Schürch; Xiang Li; Ahmed Allam; Giulia Rathmes; Amina Mollaysa; Claudia Cavelti-Weder; Michael Krauthammer (CoRR, 2023)

Dynamic Local Attention with Hierarchical Patching for Irregular Clinical Time Series. - Xingyu Chen; Xiaochen Zheng; Amina Mollaysa; Manuel Schürch; Ahmed Allam; Michael Krauthammer (CoRR, 2023)

Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes. - Cécile Trottet; Manuel Schürch; Ahmed Allam; Imon Barua; Liubov Petelytska; Oliver Distler; Anna-Maria Hoffmann-Vold; Michael Krauthammer; EUSTAR collaborators (CoRR, 2023)

Correlated product of experts for sparse Gaussian process regression. - Manuel Schürch; Dario Azzimonti; Alessio Benavoli; Marco Zaffalon (Mach. Learn., 2023)

Simple Contrastive Representation Learning for Time Series Forecasting. - Xiaochen Zheng; Xingyu Chen; Manuel Schürch; Amina Mollaysa; Ahmed Allam; Michael Krauthammer (ICASSP, 2024)

Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification. - Michael Vollenweider; Manuel Schürch; Chiara Rohrer; Gabriele Gut; Michael Krauthammer; Andreas Wicki (ML4H@NeurIPS, 2024)

Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis. - Cécile Trottet; Manuel Schürch; Ahmed Allam; Imon Barua; Liubov Petelytska; Oliver Distler; Anna-Maria Hoffmann-Vold; Michael Krauthammer (MLHC, 2024)

Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data. - Manuel Schürch; Laura Boos; Viola Heinzelmann-Schwarz; Gabriele Gut; Michael Krauthammer; Andreas Wicki; Tumor Profiler Consortium (CoRR, 2024)

Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes. - Xiaochen Zheng; Manuel Schürch; Xingyu Chen; Maria Angeliki Komninou; Reto Schüpbach; Ahmed Allam; Jan Bartussek; Michael Krauthammer (CoRR, 2024)

Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis. - Cécile Trottet; Manuel Schürch; Ahmed Allam; Imon Barua; Liubov Petelytska; Oliver Distler; Anna-Maria Hoffmann-Vold; Michael Krauthammer; EUSTAR collaborators (CoRR, 2024)