Name
Abstract | ||
|---|---|---|
We consider the task of training classifiers without labels. We propose a weakly supervised method---adversarial label learning---that trains classifiers to perform well against an adversary that chooses labels for training data. The weak supervision constrains what labels the adversary can choose. The method therefore minimizes an upper bound of the classifieru0027s error rate using projected primal-dual subgradient descent. Minimizing this bound protects against bias and dependencies in the weak supervision. Experiments on three real datasets show that our method can train without labels and outperforms other approaches for weakly supervised learning. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Learning | Training set,Subgradient method,Upper and lower bounds,Word error rate,Supervised learning,Artificial intelligence,Adversary,Classifier (linguistics),Machine learning,Mathematics,Adversarial system |
DocType | Volume | Citations |
Journal | abs/1805.08877 | 0 |
PageRank | References | Authors |
0.34 | 17 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Chidubem Arachie | 1 | 0 | 1.01 |
| Bert Huang | 2 | 563 | 39.09 |