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Learning Gaussian Processes by Minimizing PAC-Bayesian Generalization Bounds

NeurIPS 2018 · Probabilistic and scientific modeling › Uncertainty and Gaussian processes · approach: PAC-Bayes and decoupled GP learning · 21 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Train sparse GPs by minimizing the inverse-binary-KL PAC-Bayes bound instead of the VFE or FITC objectiveSparse GP regression · sarcosgeneralization upper bound: by more than a factor of 3Helped
Train sparse GPs by minimizing the inverse-binary-KL PAC-Bayes bound instead of the VFE or FITC objectiveSparse GP regression · polgeneralization upper bound: by a factor of roughly 2Helped
Train sparse GPs by minimizing the inverse-binary-KL PAC-Bayes bound instead of the VFE or FITC objectiveSparse GP regression · kin40kgeneralization upper bound: by a factor between 1.3 and 2Helped
Use the tighter inverse-binary-KL bound rather than the Pinsker-based square-root bound (ablation)PAC-GP and PAC-SGP · Regression experimentsperformanceHelped
Increase the number of inducing inputs (ablation)kl-PAC-SGP · kin40kgeneralization upper boundHelped
Increase the number of inducing inputs (ablation)kl-PAC-SGP · pol and sarcosgeneralization upper boundNo clear effect
Increase the number of inducing inputs (ablation)GP models · pol, sarcos, and kin40kMSEHelped
Replace the bounded 0-1 loss with the bounded distance-sensitive exponential loss (ablation)PAC-SGP · polMSE: improvement w.r.t. MSE of 25%Helped
Replace the bounded 0-1 loss with the bounded distance-sensitive exponential loss (ablation)PAC-SGP · sarcosMSE: little changeNo clear effect
Replace the bounded 0-1 loss with the bounded distance-sensitive exponential loss (ablation)PAC-SGP · kin40kMSE: a decline of 12%Hurt
Use ARD rather than a shared lengthscale (ablation)Sparse GP regression · pol and kin40kperformanceHelped
Use VFE rather than FITC (ablation)Sparse GP regression · pol, sarcos, and kin40kMSEHelped

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