multiplier model
Approximate Heavily-ConstrainedLearningwith LagrangeMultiplierModels
Therefore, itmay be necessary to impose aseparate constraint for each such intersection.
Country:
- North America > United States (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
Technology:
Approximate Heavily-ConstrainedLearningwith LagrangeMultiplierModels
Therefore, itmay be necessary to impose aseparate constraint for each such intersection.
Country:
- North America > United States (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
Country:
- North America > United States > California > Alameda County > Berkeley (0.04)
- North America > Canada > British Columbia > Metro Vancouver Regional District > Vancouver (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Asia > Middle East > Jordan (0.04)
Technology:
- Information Technology > Artificial Intelligence > Representation & Reasoning (1.00)
- Information Technology > Artificial Intelligence > Machine Learning > Performance Analysis > Accuracy (0.70)
- Information Technology > Artificial Intelligence > Machine Learning > Statistical Learning > Gradient Descent (0.47)
Approximate Heavily-Constrained Learning with Lagrange Multiplier Models
In some cases, however, it may be desirable to include an extremely large number of constraints.
Country:
- North America > United States > California > Alameda County > Berkeley (0.04)
- North America > Canada (0.04)
- Europe > United Kingdom > England > Cambridgeshire > Cambridge (0.04)
- Asia > Middle East > Jordan (0.04)
Technology:
We provide a comparison to the prior
We thank the reviewers for the detailed feedback. R1 and R2 ask for a comparison to a prior method. R3's main comments are about the theoretical results. We've addressed all of this below. We are thus able to handle a more general set of constraints, including those for ranking fairness.