Research map / Learning theory and algorithms / Prediction and decision losses
Predictive Multiplicity in Classification
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Measure the full 1%-level set of linear classifiers with exact MIP tools instead of a pool of penalized logistic-regression models | COMPAS arrest · COMPAS arrest, 1%-level set | discrepancy: 7% -> 17% | Mixed |
| Measure the full 1%-level set of linear classifiers with exact MIP tools instead of a pool of penalized logistic-regression models | COMPAS arrest · COMPAS arrest, 1%-level set | ambiguity: 10% -> 44% | Mixed |
| Compare ambiguity across demographic groups | COMPAS violent · COMPAS violent, 1%-level set | ambiguity: 72.9% for African-Americans; 37.2% for Caucasians | Mixed |
Closest papers by meaning
- Dropout-Based Rashomon Set Exploration for Efficient Predictive Multiplicity Estimation (ICLR 2024 · similarity 0.86)
- Quantifying Prediction Consistency Under Fine-tuning Multiplicity in Tabular LLMs (ICML 2025 · similarity 0.85)
- Reconciling Model Multiplicity for Downstream Decision Making (ICLR 2025 · similarity 0.84)
- RashomonGB: Analyzing the Rashomon Effect and Mitigating Predictive Multiplicity in Gradient Boosting (NeurIPS 2024 · similarity 0.82)
- Understanding Fixed Predictions via Confined Regions (ICML 2025 · similarity 0.81)
- Random features models: a way to study the success of naive imputation (ICML 2024 · similarity 0.81)
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