1 citations · 1 across the 5 of their papers we have counts for
5 papers
Exponential Convex Calibration Dimension for the Multi-Label Jaccard Measure
Mingyuan Zhang
The per-instance Jaccard score, or intersection over union (IoU), is standard in multi-label classification and binary segmentation. With labels, its loss matrix has outc…
Exact Rank and Convex Calibration Dimension Lower Bounds for the Multi-Label F1 Loss
Mingyuan Zhang
The instance-wise measure is a central performance measure for multi-label classification. For a problem with labels, it defines a loss matrix. Previous w…
Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures
Mingyuan Zhang, Shivani Agarwal
There has been much interest in recent years in learning good classifiers from data with noisy labels. Most work on learning from noisy labels has focused on standard loss-based pe…
On the Minimax Regret in Online Ranking with Top-k Feedback
Mingyuan Zhang, Ambuj Tewari
In online ranking, a learning algorithm sequentially ranks a set of items and receives feedback on its ranking in the form of relevance scores. Since obtaining relevance scores typ…
Convex Calibrated Surrogates for the Multi-Label F-Measure
Mingyuan Zhang, Harish G. Ramaswamy, Shivani Agarwal
The F-measure is a widely used performance measure for multi-label classification, where multiple labels can be active in an instance simultaneously (e.g. in image tagging, multipl…