Research map / Probabilistic and scientific modeling / Variational and generative sampling
Nonparametric estimation of continuous DPPs with kernel methods
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use the kernel-based finite-dimensional regularized MLE with sampling approximations | Continuous DPP estimation · Synthetic continuous DPP, [0,1]^2 | intensity recovery | Helped |
| Use multiple DPP samples instead of one (ablation) | Kernel-based nonparametric MLE · Synthetic continuous DPP | intensity estimation | Helped |
| Decrease trace regularization from λ = 0.1 to λ = 0.01 (ablation) | Kernel-based nonparametric MLE · Synthetic continuous DPP, one DPP sample | intensity scale estimate | Helped |
| Use an RKHS lengthscale that is too small (ablation) | Kernel-based nonparametric MLE · Synthetic continuous DPP | constant-intensity recovery | Hurt |
| Increase trace regularization (ablation) | Kernel-based nonparametric MLE · Synthetic continuous DPP | local intensity variation recovery | Hurt |
| Use a larger DPP sample size (ablation) | Kernel-based nonparametric MLE · Synthetic continuous DPP | constant-intensity recovery | Helped |
| Approximate the correlation kernel using sampled points | Fitted continuous DPP · Synthetic continuous DPP | correlation kernel recovery | Mixed |
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