Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Incremental Variational Sparse Gaussian Process Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Update basis functions/inducing points online via stochastic mirror ascent (iVSGPR) instead of only updating inducing-function statistics (VSGPR_svi) | variational sparse GPR · kin40k, SARCOS, KUKA1, KUKA2 (nMSE after 500 iterations) | nMSE: kin40k 0.0959 -> 0.0607 (ada) | Helped |
| Adaptive line search (iVSGPR_ada) instead of fixed 5 or 10 CG calls (ablation) | iVSGPR · SARCOS, KUKA datasets | nMSE: KUKA1 J1 0.1257 -> 0.1176 (vs 10 calls) | Helped |
| Increase fixed nonlinear CG function calls from 5 to 10 per subproblem (ablation) | iVSGPR · kin40k | nMSE: 0.0648 -> 0.0608 | Helped |
| Larger minibatch size (Nm 512 vs 2048) (ablation) | iVSGPR · SARCOS joint 2 online learning | convergence rate | Helped |
| Number of line search steps in adaptive scheme decays over iterations | iVSGPR_ada · SARCOS joint 2 | function calls per subproblem | No clear effect |
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