NDCG-Optimization

Code to reproduce the results in paper "Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence". The proposed methods in this paper, SONG and K-SONG, are implemented in LibAUC.

Updates

Building on SONG and K-SONG, we add novel algorithms with faster convergence rates: Faster SONG^v1/v2/K-SONG^v1/v2. Additionally, we incorporatd Precision@K and top-K mAP optimization algorithms, validated through experiments on molecular data (code available under the Mol-exps directory).

Citation

@inproceedings{ICML:2022:NDCG,
author = {Zi-Hao Qiu and Quanqi Hu and Yongjian Zhong and Lijun Zhang and Tianbao Yang},
title = {Large-scale Stochastic Optimization of {NDCG} Surrogates for Deep Learning with Provable Convergence},
booktitle = {Proceedings of the 39th International Conference on Machine Learning (ICML)},
pages = {18122--18152},
year = {2022},
}