Research map / Learning theory and algorithms / Prediction and decision losses
Classification Under Human Assistance
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Train the SVM jointly with a choice of which training samples to outsource to humans | SVM under human assistance · Synthetic data | misclassification test error | Helped |
| Train the SVM jointly with a choice of which training samples to outsource to humans | SVM under human assistance · Synthetic data | F1 score | Helped |
| Train the SVM with human assistance at different automation levels | SVM under human assistance · Messidor, Stare, and Aptos | misclassification test error | Helped |
| Train the SVM with human assistance at different automation levels | SVM under human assistance · Messidor, Stare, and Aptos | F1 score | Helped |
| Use the randomized distorted greedy algorithm instead of deterministic distorted greedy | SVM under human assistance · Messidor, Stare, and Aptos | performance | Mixed |
| Increase the maximum number of outsourced samples when human error is small | Distorted greedy SVM · Aptos | performance | Helped |
| Outsource samples when human error is high | Distorted greedy SVM · Aptos | performance | No clear effect |
| Increase human error | Distorted greedy SVM · Synthetic linear SVM with offset | number of samples outsourced | Hurt |
Closest papers by meaning
- Regression under Human Assistance (AAAI 2020 · similarity 0.89)
- Near-Tight Margin-Based Generalization Bounds for Support Vector Machines (ICML 2020 · similarity 0.83)
- Towards Automated Semi-Supervised Learning (AAAI 2019 · similarity 0.82)
- Multi-Class Support Vector Machine with Maximizing Minimum Margin (AAAI 2024 · similarity 0.82)
- Faster Algorithms for Structured Linear and Kernel Support Vector Machines (ICLR 2025 · similarity 0.82)
- Towards Human-AI Complementarity with Prediction Sets (NeurIPS 2024 · similarity 0.81)
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