Research map / Learning theory and algorithms / Prediction and decision losses
A Minimax Approach to Supervised Learning
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use MEM instead of the strongest listed baselines | binary classification · adult | error (%): 17 -> 17 | No clear effect |
| Use MEM instead of SVM | binary classification · credit | error (%): 16 -> 12 | Helped |
| Use MEM instead of SVM | binary classification · kr-vs-kp | error (%): 3 -> 7 | Hurt |
| Use MEM instead of DCC | binary classification · promoters | error (%): 5 -> 5 | No clear effect |
| Use MEM instead of the strongest listed baselines | binary classification · votes | error (%): 3 -> 4 | Hurt |
| Use MEM instead of the strongest listed baselines | binary classification · hepatitis | error (%): 17 -> 17 | No clear effect |
| Use MEM instead of DRC | synthetic binary classification with n=200 and d=10000 · synthetic (n=200, d=10000) | error rate: 20.4 -> 20.0 | Helped |
| Use MEM instead of SVM | synthetic binary classification with n=200 and d=10000 · synthetic (n=200, d=10000) | error rate: 20.6 -> 20.0 | Helped |
Closest papers by meaning
- The Performance Analysis of Generalized Margin Maximizers on Separable Data (ICML 2020 · similarity 0.88)
- Minimax Classification with 0-1 Loss and Performance Guarantees (NeurIPS 2020 · similarity 0.87)
- On the Efficiency of ERM in Feature Learning (NeurIPS 2024 · similarity 0.86)
- Minimax Optimal Early-Stopped Gradient Descent for Gaussian Mixture Classification (NeurIPS 2026 · similarity 0.85)
- Generalization Analysis of Machine Learning Algorithms via the Worst-Case Data-Generating Probability Measure (AAAI 2024 · similarity 0.85)
- Sequential Probability Assignment with Contexts: Minimax Regret, Contextual Shtarkov Sums, and Contextual Normalized Maximum Likelihood (NeurIPS 2024 · similarity 0.85)
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