A Unifying Framework for Gaussian Process Pseudo-Point Approximations using Power Expectation Propagation

By Thang D. Bui, Josiah Yan, Richard E. Turner. 2016

146 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Materials Chemistry, Molecular Biology

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Citations in the corpus

Variational Auto-Regressive Gaussian Processes for Continual Learning (2020, methodology): “This section focuses on pseudo-point based methods [23, 4] and, in particular, the seminal work of Titsias [36], Hensman et al.”
Functional Regularisation for Continual Learning using Gaussian Processes (2019, methodology): “…on sparse GP methods (Csato & Opper, 2002; Lawrence et al., 2002; Seeger et al., 2003; Quiñonero-Candela & Rasmussen, 2005; Snelson & Ghahramani, 2006; Titsias, 2009; Hensman et al., 2013; Bui et al., 2017b), which summarise posterior distributions using small numbers of inducing points.”
Streaming Sparse Gaussian Process Approximations (2017, background): “This prohibitive complexity of exact learning and inference in GP models has driven the development of many sparse approximation frameworks [12, 13].”
Continual Multi-task Gaussian Processes (2019, methodology): “Importantly, we condition every observed output y n to the inﬁnite-dimensional function space f similarly to Bui et al. (2017b), having p ( y n | f ) instead.”
Partitioned Variational Inference: A unified framework encompassing federated and continual learning (2018, methodology): “…for various models and applications [Broderick et al., 2013, Li and Hoiem, 2016, Kirkpatrick et al., 2017, Zenke et al., 2017, Seff et al., 2017, Bui et al., 2017a, Nguyen et al., 2018, Zeno et al., 2018, Chaudhry et al., 2018], to setting up relevant metrics and benchmarks for evaluation…”
State Space Gaussian Processes with Non-Gaussian Likelihood (2018, background): “…& Ras-mussen, 2005) based on variational free energy (VFE, Tit-sias, 2009), fully independent training conditionals (FITC, Snelson & Ghahramani, 2006), hybrids (Bui et al., 2017), or stochastic approximations (Hensman et al., 2013; Krauth et al., 2017) applicable to any data dimension D .”
When Gaussian Process Meets Big Data: A Review of Scalable GPs (2018, methodology): “The interdomain idea has also been applied to the posterior approximations [71], [76], [172].”