Incremental Variational Sparse Gaussian Process Regression

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Continual Learning via Sequential Function-Space Variational Inference (2023, methodology): “Incremental variational sparse GP regression (Cheng and Boots, 2016), streaming sparse GP s (Bui et al., 2017) and online sparse multi-output GP regression (Yang et al., 2019) built on the work of Csat´o and Opper (2002) and Csat´o (2002), and are effective approaches to continual learning for…”
Streaming Sparse Gaussian Process Approximations (2017, background): “Alternatively, stochastic optimisation may be applied directly to the original, uncollapsed version of the bound [4, 15].”
Continual Multi-task Gaussian Processes (2019, background): “In terms of scalability for single-output GP models, both Cheng and Boots (2016) and Bui et al. (2017a) extended online learning methods and uncertainty propagation to the popular variational inference setup of sparse GP approximations.”
When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, background): “Moreover, the stochastic variants naturally showcase the online structure [215], since the bound in (17) supports minibatch learning by stochastic optimization.”