9 citations · 10 across the 5 of their papers we have counts for
5 papers
Two-Stage Aggregation with Dynamic Local Attention for Irregular Time Series
Xingyu Chen, Xiaochen Zheng, Amina Mollaysa +3
Irregular multivariate time series data is characterized by varying time intervals between consecutive observations of measured variables/signals (i.e., features) and varying sampl…
Generating Personalized Insulin Treatments Strategies with Deep Conditional Generative Time Series Models
Manuel Schürch, Xiang Li, Ahmed Allam +4
We propose a novel framework that combines deep generative time series models with decision theory for generating personalized treatment strategies. It leverages historical patient…
Simple Contrastive Representation Learning for Time Series Forecasting
Xiaochen Zheng, Xingyu Chen, Manuel Schürch +3
Contrastive learning methods have shown an impressive ability to learn meaningful representations for image or time series classification. However, these methods are less effective…
Correlated Product of Experts for Sparse Gaussian Process Regression
Manuel Schürch, Dario Azzimonti, Alessio Benavoli +1
Gaussian processes (GPs) are an important tool in machine learning and statistics with applications ranging from social and natural science through engineering. They constitute a p…
Orthogonally Decoupled Variational Fourier Features
Dario Azzimonti, Manuel Schürch, Alessio Benavoli +1
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 approximations of the covariance…