Our doubly mixed-effects GP models the complex inter- task and inter-sample relationships and decomposes the input effects on the output functions into four com ...
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May 3, 2022 · We address the multi-task Gaussian process (GP) regression problem with the goal of decomposing input effects on outputs into components shared ...
Doubly mixed-effects Gaussian process (DMGP) is a multi-task GP regression model that learns fixed and random effects across both samples and tasks ...
This work proposes a family of mixed-effects GPs, including doubly and translated mixed-effects GPs, that performs a decomposition of input effects on outputs ...
Nov 18, 2018 · The problem I have is to run a model with some random effects, and also have a Gaussian process on time.
The linear mixed-effects model (LMM) is widely used in the analysis of clustered or longitudinal data. This pa- per aims to address analytic challenges ...
Apr 8, 2024 · In this article, we propose an asynchronous doubly stochastic gradient algorithm to handle the large-scale training of GPR.
We evaluate Gaussian process regression via doubly stochastic gradient descent (GPR-DSGD) by learning a model of inverse dynamics for torque control on a 7-DOF.
Missing: Mixed- Effects
Gaussian processes (GPs) are a good choice for function approximation as they are flexible, robust to over-fitting, and provide well-calibrated predictive ...
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One simple way to define a mixture model is to choose a mixture component at top-level, and define each component in a separate code path.
Missing: Mixed- | Show results with:Mixed-