Blog Posts

Please refer to my Zhihu (知乎) account for latest updates.

• Overview / Thoughts / Resources
  1. Collection of imbalanced learning papers, codes, frameworks, and libraries.
  2. An overview of imbalanced learning in real-world practical applications: S01.
  3. An overview of imbalanced learning in real-world practical applications: S02.
  4. The influence of minority class examples on learning from imbalanced data
• Short Introduction to
  1. A short introduction to learning to teach.
  2. A short introduction to learning to learn and meta-learning.
  3. A short introduction to curriculum learning and self-paced learning.
• Reading Notes
  1. [NeurIPS 2020] MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler
  2. [ICDE 2020] Self-paced Ensemble for Highly Imbalanced Massive Data Classification
  3. [NIPS 2019] Learning Data Manipulation for Augmentation and Weighting
  4. [CVPR2019] Class-Balanced Loss Based on Effective Number of Samples