The following pages link to PAC learning with nasty noise. (Q1853516):
Displaying 16 items.
- Learning with unreliable boundary queries (Q1271553) (← links)
- On-line learning with malicious noise and the closure algorithm (Q1273754) (← links)
- Algorithms for strategyproof classification (Q1761283) (← links)
- Apple tasting. (Q1854360) (← links)
- Can PAC learning algorithms tolerate random attribute noise? (Q1894713) (← links)
- Fat-shattering and the learnability of real-valued functions (Q1924381) (← links)
- Incentive compatible regression learning (Q1959425) (← links)
- Machine learning in adversarial environments (Q1959613) (← links)
- Learning under \(p\)-tampering poisoning attacks (Q2202514) (← links)
- PAC-learning in the presence of one-sided classification~noise (Q2254605) (← links)
- A model for prejudiced learning in noisy environments (Q2572686) (← links)
- Efficient noise-tolerant learning from statistical queries (Q3158527) (← links)
- Sample-efficient strategies for learning in the presence of noise (Q3158555) (← links)
- Some Recent Results on Local Testing of Sparse Linear Codes (Q4933384) (← links)
- Analyzing robustness of Angluin's \(L^*\) algorithm in presence of noise (Q6563046) (← links)
- Learning from fuzzy labels: theoretical issues and algorithmic solutions (Q6577647) (← links)
