这篇论文搞了个新损失函数CoCo，训练收敛快，在OpenML-CC18上跟SVM打成平手，做表格分类任务的可以试试。
论文提出CoCo损失函数，通过鼓励类内坍缩和类间对比来学习归一化且结构良好的表示。理论分析表明，CoCo相比点回归和交叉熵损失具有更接近最优配置的初始化、更有信息量的梯度和更强的类坍缩激励。在OpenML-CC18基准的多个表格数据集上，CoCo实现了与核SVM、随机森林、点回归和基于交叉熵的神经网络相当的性能。实验还证明CoCo促进了更紧的类聚类和更快的收敛速度。这些结果表明CoCo损失在保持竞争性预测性能的同时，能有效学习判别性表示。
Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence
In this work, we introduce CoCo, a loss function aimed at learning normalized and well-structured representations. The proposed loss encourages intra-class collapse and inter-class contrast while preserving sufficient flexibility for neural networks to approximate geometrically optimal embeddings with large angular separation between classes. We provide a theoretical analysis positioning CoCo with respect to related objectives such as dot regression and cross-entropy, showing that the new proposed loss benefits from closer initialization to the optimal configuration, more informative gradients, and stronger incentives for class-wise representation collapse. Extensive experiments on diverse tabular datasets from the OpenML-CC18 benchmark show that CoCo achieves competitive performance with state-of-the-art methods, including kernel SVM, Random Forest, dot regression, and cross-entropy-based neural networks. In addition, both theoretical arguments and empirical analyses demonstrate that the proposal promotes tighter class clustering and faster convergence. These results highlight CoCo loss as an effective objective for learning discriminative representations while maintaining competitive predictive performance.