Research map / Learning theory and algorithms / Sample complexity
High-Dimensional Structured Quantile Regression
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use quantile loss instead of least squares (Lasso) for sparse regression with heavy-tailed noise | l1 penalized regression · Student t-distribution noise | estimation error | Helped |
| Use quantile loss instead of least squares (Lasso) under outlier contamination | l1 penalized regression · Percent contamination 2.5% to 15% | estimation error | Helped |
| Increase number of samples beyond the NIPS threshold (about s log p for l1) | structured quantile regression · Synthetic l1 and l1/l2 group sparse recovery | probability of recovering true parameter | Helped |
| Assess quantile regression with n < p and no regularization | quantile regression without structure · high-dimensional n<p | number of interpolated samples | No clear effect |
Closest papers by meaning
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