Research map / Learning theory and algorithms / Prediction and decision losses
Online Active Learning of Reject Option Classifiers
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Use DRAL or DSAL active querying instead of DSOL online learning | Reject option classification · Gisette | risk at a given number of queried labels | Helped |
| Use DRAL or DSAL active querying instead of DSOL online learning | Reject option classification · Phishing | risk at a given number of queried labels | Helped |
| Use DSAL active querying instead of DSOL online learning | Reject option classification · Guide | risk at a given number of queried labels | Helped |
| Use DSAL instead of DRAL | Reject option classification · Gisette | risk at a given number of queried labels | Helped |
| Use DSAL instead of DRAL | Reject option classification · Guide | risk at a given number of queried labels | Helped |
| Use DSAL instead of DRAL | Reject option classification · Phishing | risk at a given number of queried labels | No clear effect |
| Use DSAL instead of DRAL | Reject option classification · Gisette and Phishing | average risk | Hurt |
| Use DSAL instead of DRAL | Reject option classification · Guide | average risk | Helped |
| Use DSAL instead of DSOL | Reject option classification · Gisette and Guide | average risk | No clear effect |
| Use DSAL instead of DSOL | Reject option classification · Phishing | average risk | Hurt |
| Use DRAL instead of DSOL | Reject option classification · Phishing | average risk | Helped |
| Use DSAL instead of DRAL | Reject option classification · Gisette and Phishing | fraction of queried labels | Helped |
Closest papers by meaning
- Sparse Reject Option Classifier Using Successive Linear Programming (AAAI 2019 · similarity 0.86)
- On the Convergence of Loss and Uncertainty-based Active Learning Algorithms (NeurIPS 2024 · similarity 0.82)
- Universal Rates for Active Learning (NeurIPS 2024 · similarity 0.82)
- SEL-BALD: Deep Bayesian Active Learning with Selective Labels (NeurIPS 2024 · similarity 0.81)
- Active Learning From Positive and Unlabeled Examples (NeurIPS 2026 · similarity 0.81)
- Online Linear Classification with Massart Noise (ICML 2025 · similarity 0.81)
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