Research map / Learning theory and algorithms / Kernels and regression theory
An Adaptive Test of Independence with Analytic Kernel Embeddings
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Optimize kernel widths and test locations on a tuning split instead of using median widths and random locations (ablation) | NFSIC · Million Song Data, n=500 | probability of rejecting H0: 0.4 to 0.8 | Helped |
| Optimize test locations and kernel widths (ablation) | NFSIC · Sinusoid, ω=1,...,6 | test power | Helped |
| Increase the number of randomly drawn test locations from 1 to 10 (ablation) | NFSIC · Sinusoid, ω=2, n=800 | test power: roughly 0.8 | Helped |
| Increase the number of randomly drawn test locations beyond 400 (ablation) | NFSIC · Sinusoid, ω=2, n=800 | test power | Hurt |
| Use random rather than optimized test locations (ablation) | NFSIC · Sinusoid | test power | Hurt |
| Use a fixed, randomly drawn finite set of test locations (ablation) | NFSIC · Dependent distributions | test power | Helped |
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