First-Order Optimization for Superquantile-Based Supervised Learning-Reference-Cited by-同舟云学术

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First-Order Optimization for Superquantile-Based Supervised Learning

Published:2020-09 Issue: Volume: Page:
ISSN:
Container-title:2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
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Author:

Laguel Yassine,Malick Jerome,Harchaoui Zaid

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9217888/9231523/09231909.pdf?arnumber=9231909

Cited by 5 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Chance-constrained programs with convex underlying functions: a bilevel convex optimization perspective;Computational Optimization and Applications;2024-04-27

2. Federated learning with superquantile aggregation for heterogeneous data;Machine Learning;2023-05-16

3. Fast and Stable Convergence of Online SGD for CV@R-Based Risk-Aware Learning;ICASSP 2022 - 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP);2022-05-23

4. Superquantile-Based Learning: A Direct Approach Using Gradient-Based Optimization;Journal of Signal Processing Systems;2022-01-11

5. Superquantiles at Work: Machine Learning Applications and Efficient Subgradient Computation;Set-Valued and Variational Analysis;2021-12

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