Optimization for Machine Learning and AI's repositories
ICCV2021_DeepAUC
Official implementation of the paper "Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification, ICCV2021"
NeurIPS2021_SOAP
Official implementation of the paper "Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence" published on Neurips2021.
ICML2021_FedDeepAUC_CODASCA
Official implementation: "Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity", ICML2021.
ICML2023_BSVRB
Official implementation of the paper "Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization", ICML 2023
ICML2023_FeDXL
Official implementation of ICML 2023 paper "FeDXL: Provable Federated Learning for Deep X-Risk Optimization".
NDCG-Optimization
Official implementation of the paper "Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence" ICML2022.
ICML2023_LDR
The official implementation from 'Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity' ICML2023