Imbalanced learning constitutes one of the most formidable challenges within data mining
and machine learning. Despite continuous research advancement over the past decades …

Data imbalance exists ubiquitously in real-world visual regressions, eg, age estimation and
pose estimation, hurting the model's generalizability and fairness. Thus, imbalanced …

Real-world data often exhibit imbalanced distributions, where certain target values have
significantly fewer observations. Existing techniques for dealing with imbalanced data focus …

Estimating heterogeneous treatment effects (HTE) is crucial for identifying the variation of
treatment effects across individuals or subgroups. Most existing methods estimate HTE by …

A number of computer vision deep regression approaches report improved results when
adding a classification loss to the regression loss. Here, we explore why this is useful in …

Imbalanced problems can arise in different real-world situations, and to address this, certain
strategies in the form of resampling or balancing algorithms are proposed. This issue has …

Combining high‐throughput experiments with machine learning accelerates materials and
process optimization toward user‐specified target properties. In this study, a rapid machine …

**年来不**衡分类问题受到广泛关注. SMOTE 过采样通过添加生成的少数类样本改变不**衡
数据集的数据分布, 是改善不**衡数据分类模型性能的流行方法之一. 本文首先阐述了SMOTE …

Accurate ultra-short-term air conditioning (AC) load prediction is essential for the optimal
control of split air conditioners. However, the distribution of the AC load from different …

Existing regression models tend to fall short in both accuracy and uncertainty estimation
when the label distribution is imbalanced. In this paper, we propose a probabilistic deep …