Search Results - "Schürch, Manuel"

Correlated product of experts for sparse Gaussian process regression by Schürch, Manuel, Azzimonti, Dario, Benavoli, Alessio, Zaffalon, Marco

“…Gaussian processes (GPs) are an important tool in machine learning and statistics. However, off-the-shelf GP inference procedures are limited to datasets with…”
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Recursive estimation for sparse Gaussian process regression by Schürch, Manuel, Azzimonti, Dario, Benavoli, Alessio, Zaffalon, Marco

“…Gaussian Processes (GPs) are powerful kernelized methods for non-parametric regression used in many applications. However, their use is limited to a few…”
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Abstract B053: NucAE: Autoencoder-based enhancement of nucleosome occupancy signals from low-coverage cfDNA sequencing by Balázs, Zsolt, Radig, Jean, Wolford, Noah, Schürch, Manuel, Krauthammer, Michael

“…Abstract Introduction: Cell-free DNA (cfDNA) is emerging as a valuable cancer biomarker, enabling both the detection and epigenetic characterization of…”
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Learning Personalized Treatment Decisions in Precision Medicine: Disentangling Treatment Assignment Bias in Counterfactual Outcome Prediction and Biomarker Identification by Vollenweider, Michael, Schürch, Manuel, Rohrer, Chiara, Gut, Gabriele, Krauthammer, Michael, Wicki, Andreas

“…Precision medicine has the potential to tailor treatment decisions to individual patients using machine learning (ML) and artificial intelligence (AI), but it…”
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Towards AI-Based Precision Oncology: A Machine Learning Framework for Personalized Counterfactual Treatment Suggestions based on Multi-Omics Data by Schürch, Manuel, Boos, Laura, Heinzelmann-Schwarz, Viola, Gut, Gabriele, Krauthammer, Michael, Wicki, Andreas, Consortium, Tumor Profiler

“…AI-driven precision oncology has the transformative potential to reshape cancer treatment by leveraging the power of AI models to analyze the interaction…”
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Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series by Chen, Xingyu, Zheng, Xiaochen, Mollaysa, Amina, Schürch, Manuel, Allam, Ahmed, Krauthammer, Michael

“…Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e.,…”
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Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models by Schürch, Manuel, Li, Xiang, Allam, Ahmed, Rathmes, Giulia, Mollaysa, Amina, Cavelti-Weder, Claudia, Krauthammer, Michael

“…Machine Learning for Health (ML4H) 2023 We propose a novel framework that combines deep generative time series models with decision theory for generating…”
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Correlated Product of Experts for Sparse Gaussian Process Regression by Schürch, Manuel, Azzimonti, Dario, Benavoli, Alessio, Zaffalon, Marco

“…Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through…”
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Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes by Zheng, Xiaochen, Schürch, Manuel, Chen, Xingyu, Komninou, Maria Angeliki, Schüpbach, Reto, Allam, Ahmed, Bartussek, Jan, Krauthammer, Michael

“…The identification of phenotypes within complex diseases or syndromes is a fundamental component of precision medicine, which aims to adapt healthcare to…”
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Simple Contrastive Representation Learning for Time Series Forecasting by Zheng, Xiaochen, Chen, Xingyu, Schürch, Manuel, Mollaysa, Amina, Allam, Ahmed, Krauthammer, Michael

“…Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these…”
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Simple Contrastive Representation Learning for Time Series Forecasting by Zheng, Xiaochen, Chen, Xingyu, Schurch, Manuel, Mollaysa, Amina, Allam, Ahmed, Krauthammer, Michael

“…Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these…”
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Orthogonally Decoupled Variational Fourier Features by Azzimonti, Dario, Schürch, Manuel, Benavoli, Alessio, Zaffalon, Marco

“…Sparse inducing points have long been a standard method to fit Gaussian processes to big data. In the last few years, spectral methods that exploit…”
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Recursive Estimation for Sparse Gaussian Process Regression by Schürch, Manuel, Azzimonti, Dario, Benavoli, Alessio, Zaffalon, Marco

“…Gaussian Processes (GPs) are powerful kernelized methods for non-parameteric regression used in many applications. However, their use is limited to a few…”
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Modeling Complex Disease Trajectories using Deep Generative Models with Semi-Supervised Latent Processes by Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, collaborators, the EUSTAR

“…In this paper, we propose a deep generative time series approach using latent temporal processes for modeling and holistically analyzing complex disease…”
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Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis by Trottet, Cécile, Schürch, Manuel, Allam, Ahmed, Barua, Imon, Petelytska, Liubov, Launay, David, Airò, Paolo, Bečvář, Radim, Denton, Christopher, Radic, Mislav, Distler, Oliver, Hoffmann-Vold, Anna-Maria, Krauthammer, Michael, collaborators, the EUSTAR

“…We propose a deep generative approach using latent temporal processes for modeling and holistically analyzing complex disease trajectories, with a particular…”
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