Research map / Learning theory and algorithms / Sample complexity
Composite Marginal Likelihood Methods for Random Utility Models
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use RBCML with uniform rank-breaking and uniform CML weights instead of GMM | Gaussian RUMs · Gaussian RUMs, synthetic full rankings, 10 alternatives | n×MSE | Helped |
| Use RBCML with uniform rank-breaking and uniform CML weights instead of GMM | Gaussian RUMs · Gaussian RUMs, synthetic full rankings, 10 alternatives | running time | Helped |
| Use 2-LSR instead of RBCML with heuristic weights | Plackett-Luce · Plackett-Luce, synthetic full rankings, large numbers of rankings | n×MSE | Helped |
| Use RBCML with heuristic weights instead of K-O Breaking | Plackett-Luce · Plackett-Luce, synthetic full rankings, large numbers of rankings | n×MSE | Helped |
| Use RBCML with heuristic weights instead of K-O Breaking | Plackett-Luce · Plackett-Luce, synthetic full rankings, large numbers of rankings | running time | Hurt |
| Use RBCML with heuristic weights instead of 2-LSR | Plackett-Luce · Plackett-Luce, synthetic full rankings, large numbers of rankings | running time | Helped |
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