We study a generalization of boosting to the multiclass setting. We introduce a weak learning condition for multiclass classification that captures the original notion ofweak learnability asbeing "slightly better than random guessing".

To address these shortcomings, we introduce a pioneering technique that replaces the spatial model with a topological one within the RANSAC process.

In recent years, multi-objective optimization (MOO) emerges as a foundational problem underpinning many multi-agent multi-task learning applications.

Bilinear feature transformation has shown the state-of-the-art performance in learning fine-grained image representations.

In active learning (AL), we focus on reducing the data annotation cost from the model training perspective. However, "testing", which often refers to the model

This paper studies statistical characteristics of multivariate observations with irregular changes in their covariance structures across input space.