LibAUC 1.3.0

Introducing LibAUC 1.3.0

We are thrilled to release LibAUC 1.3.0! In this version, we have made improvements and brought new features to our library. We have released a new documentation website at https://docs.libauc.org/, where you can access our code and comments. We are also happy to announce that our LibAUC paper has been accepted by KDD2023!

Major Improvements

• Improved the implementations for DualSampler and TriSampler for better efficiency.
• Merged DataSampler for NDCGLoss with TriSampler and added a new string argument mode to switch between classification mode for multi-label classification and ranking mode for movie recommendations.
• Improved AUCMLoss and included a new version v2 (required DualSampler) that removes the class prior p required in the previous version v1. To use different version, you can set version='v1' or version='v2' in AUCMLoss.
• Improved CompositionalAUCLoss, which now allows multiple updates for optimizing inner loss by setting k in the loss. Similar to AUCMLoss, we introduced v2 version in this loss without using the class prior p. By default, k is 1 and version is v1.
• Improved code quality for APLoss and pAUCLoss including pAUC_CVaR_Loss, pAUC_DRO_Loss, tpAUC_KL_Loss for better efficiency and readability.
• API change for all optimizer methods. Please pass model.parameters() to the optimizer instead of model, e.g., PESG(model.parameters()).

New Features

• Launched an official documentation site at http://docs.libauc.org/ to access source code and parameter information.
• Introduced a new library logo for X-Risk designed by Zhuoning Yuan, Tianbao Yang .
• Introduced MIDAM for multi-instance learning. It supports two pooling functions, MIDAMLoss('softmax') for using softmax pooling and MIDAMLoss('attention') for attention-based pooling.
• Introduced a new GCLoss wrapper for contrastive self-supervised learning, which can be optimized by two algorithms in the backend: SogCLR and iSogCLR.
• Introduced iSogCLR for automatic temperature individualization in self-supervised contrastive learning. To use iSogCLR, you can set GCLoss('unimodal', enable_isogclr=True) and GCLoss('bimodal', enable_isogclr=True).
• Introduced three new multi-label losses: mAPLoss for optimizing mean AP, MultiLabelAUCMLoss for optimizing multi-label AUC loss, and MultiLabelpAUCLoss for multi-label partial AUC loss.
• Introduced PairwiseAUCLoss to support optimization of traditional pairwise AUC losses.
• Added more evaluation metrics: ndcg_at_k, map_at_k, precision_at_k, and recall_at_k.

Acknowledgment

Team: Zhuoning Yuan, Dixian Zhu, Zi-Hao Qiu, Gang Li, Tianbao Yang (Advisor)

Feedback

We value your thoughts and feedback! Please fill out this brief survey to guide our future developments. Thank you for your time! For other questions, please contact us @ Zhuoning Yuan [yzhuoning@gmail.com] and Tianbao Yang [tianbao-yang@tamu.edu].