Research map / Probabilistic and scientific modeling / Uncertainty and Gaussian processes
Input Dependent Sparse Gaussian Processes
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use input-conditioned neural networks to generate inducing locations and variational parameters instead of shared inducing points | Sparse variational Gaussian processes · UCI regression datasets | negative test log-likelihood: 1.550 (0.172) average rank vs 2.475 (0.156) | Helped |
| Use input-conditioned inducing locations and variational parameters | Sparse variational Gaussian processes · UCI classification datasets | negative test log-likelihood: 2.625 (0.155) average rank vs 1.775 (0.158) | No clear effect |
| Use input-conditioned inducing locations and variational parameters | Sparse variational Gaussian processes · UCI regression datasets | training time per epoch: 591.7 (0.58) -> 190.3 (0.75) | Helped |
| Use input-conditioned inducing locations and variational parameters | Sparse variational Gaussian processes · UCI regression datasets | prediction time per epoch: 0.9(0.00) -> 0.3(0.00) | Helped |
| Use input-conditioned inducing locations and variational parameters | Sparse variational Gaussian processes · UCI regression datasets | RMSE: 1.500 (0.151) average rank vs 2.400 (0.138) | Helped |
| Use input-conditioned inducing locations and variational parameters | Sparse variational Gaussian processes · UCI classification datasets | accuracy: 2.650 (0.168) average rank vs 1.975 (0.152) | No clear effect |
| Increase the number of inducing points above M=3 (ablation) | IDSGP · UCI classification datasets | prediction performance | No clear effect |
| Use input-conditioned inducing locations and variational parameters | Sparse variational Gaussian processes · UCI classification datasets | training time per epoch: 1547(145) -> 1143(100) | Helped |
Closest papers by meaning
- Sparse within Sparse Gaussian Processes using Neighbor Information (ICML 2021 · similarity 0.95)
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- Variational Gaussian Processes with Decoupled Conditionals (NeurIPS 2023 · similarity 0.91)
- Robust Gaussian Processes via Relevance Pursuit (NeurIPS 2024 · similarity 0.88)
- Sparse Inducing Points in Deep Gaussian Processes: Enhancing Modeling with Denoising Diffusion Variational Inference (ICML 2024 · similarity 0.88)
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