Research map / Learning theory and algorithms / Sample complexity
Distributionally Robust Parametric Maximum Likelihood Estimation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use parametric DRO instead of MLE | Poisson regression · Simulated data, N=50 | CVaR_5%: 0.4906 -> 0.0547 | Helped |
| Use parametric DRO instead of MLE | Poisson regression · Simulated data, N=100 | CVaR_5%: 0.1651 -> 0.0518 | Helped |
| Use parametric DRO instead of MLE | Poisson regression · Simulated data, N=500 | CVaR_5%: 0.0246 -> 0.0172 | Helped |
| Add uncertainty in the conditional distributions using conditional KL radii | DRO Poisson regression · Simulated data, N=50 | out-of-sample divergence: 17.65% | Helped |
| Add uncertainty in the conditional distributions using conditional KL radii | DRO Poisson regression · Simulated data, N=100 | out-of-sample divergence: 10.55% | Helped |
| Add uncertainty in the conditional distributions using conditional KL radii | DRO Poisson regression · Simulated data, N=500 | out-of-sample divergence: 1.82% | Helped |
| Use parametric DRO instead of L1 regularization | Logistic regression · climate | AUC: 94.85 -> 94.30 | Hurt |
| Use parametric DRO instead of L1 regularization | Logistic regression · haberman | AUC: 69.19 -> 66.86 | Hurt |
| Use parametric DRO instead of L1 regularization | Logistic regression · housing | AUC: 75.37 -> 76.24 | Helped |
| Use parametric DRO instead of the strongest listed baseline | Logistic regression · UCI datasets | AUC | Mixed |
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