Research map / Learning theory and algorithms / Sample complexity
The Well-Tempered Lasso
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Add Gaussian noise with 1/SNR=10 to the synthetic worst-case regression matrix | Exact Lasso path, d=10 · Synthetic worst-case regression | path segments: 29525 -> 268 | Helped |
| Add Gaussian noise with 1/SNR=10 to the synthetic worst-case regression matrix | Exact Lasso path, d=7 · Synthetic worst-case regression | path segments: 1094 -> 256 | Helped |
| Add Gaussian noise with 1/SNR=10 to the synthetic worst-case regression matrix | Exact Lasso path, d=4 · Synthetic worst-case regression | path segments: 41 -> 31 | Helped |
| Add Gaussian noise with 1/SNR=20 to the synthetic worst-case regression matrix | Exact Lasso path, d=10 · Synthetic worst-case regression | path segments: 29525 -> 8592 | Helped |
| Add a tiny amount of Gaussian noise to the synthetic worst-case data matrix | Exact Lasso path · Synthetic worst-case regression | path segments: 41 -> 41; 29525 -> 268 | Mixed |
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