optimization-ai Goto Github PK
Name: Optimization for Machine Learning and AI
Type: Organization
Bio: OptMAI Lab at Texas A&M University directed by Professor Tianbao Yang
Name: Optimization for Machine Learning and AI
Type: Organization
Bio: OptMAI Lab at Texas A&M University directed by Professor Tianbao Yang
Official implementation of the paper "Robust Deep AUC Maximization: A New Surrogate Loss and Empirical Studies on Medical Image Classification, ICCV2021"
Official implementation: "Federated Deep AUC Maximization for Heterogeneous Data with a Constant Communication Complexity", ICML2021.
Official implementation of the paper "Blockwise Stochastic Variance-Reduced Methods with Parallel Speedup for Multi-Block Bilevel Optimization", ICML 2023
Official implementation of ICML 2023 paper "FeDXL: Provable Federated Learning for Deep X-Risk Optimization".
The official implementation from 'Label Distributionally Robust Losses for Multi-class Classification: Consistency, Robustness and Adaptivity' ICML2023
LibAUC: A Deep Learning Library for X-Risk Optimization
Official implementation of the paper "Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence" ICML2022.
Official implementation of the paper "Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence" published on Neurips2021.
Official implementation of the paper "Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance", ICML2022.
To Cool or not to Cool? Temperature Network Meets Large Foundation Models via DRO
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