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Distributionally Robust Optimization (DRO), as a popular method to train robust models against distribution shift between training and test sets, has received tremendous attention in recent years.
Problem Complexity and Method Efficiency in Optimization
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Convex optimization
Stephen Boyd, Stephen P Boyd, and Lieven Vandenberghe · 2004
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Subgradient methods for saddle-point problems
Angelia Nedić and Asuman Ozdaglar · 2009
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Robust stochastic approximation approach to stochastic programming
Arkadi Nemirovski, Anatoli Juditsky, Guanghui Lan, and Alexander Shapiro · 2009
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Distributionally robust optimization under moment uncertainty with application to data-driven problems
Erick Delage and Yinyu Ye · 2010
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Solving variational inequalities with stochastic mirror-prox algorithm
Anatoli Juditsky, Arkadi Nemirovski, and Claire Tauvel · 2011
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Entropic value-at-risk: A new coherent risk measure
Amir Ahmadi-Javid · 2012
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Attentional biased stochastic gradient for imbalanced classification
Qi Qi, Yi Xu, Rong Jin, Wotao Yin, and Tianbao Yang · 2012
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Robust solutions of optimization problems affected by uncertain probabilities
Aharon Ben-Tal, Dick Den Hertog, Anja De Waegenaere, Bertrand Melenberg, and Gijs Rennen · 2013
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Convex optimization in julia
Madeleine Udell, Karanveer Mohan, David Zeng, Jenny Hong, Steven Diamond, and Stephen Boyd · 2014
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Statistics of robust optimization: A generalized empirical likelihood approach
C. John Duchi, W. Peter Glynn, and Hongseok Namkoong · 2016
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Stochastic gradient methods for distributionally robust optimization with f-divergences
Hongseok Namkoong and John C Duchi · 2016
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Statistical estimation of composite risk functionals and risk optimization problems
Darinka Dentcheva, Spiridon Penev, and Andrzej Ruszczynski · 2017
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Variance-based regularization with convex objectives
Hongseok Namkoong and John C Duchi · 2017
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Stochastic compositional gradient descent: algorithms for minimizing compositions of expected-value functions
Mengdi Wang, Ethan X Fang, and Han Liu · 2017
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Data-driven robust optimization
Dimitris Bertsimas, Vishal Gupta, and Nathan Kallus · 2018
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A robust learning approach for regression models based on distributionally robust optimization
Ruidi Chen and Ioannis C Paschalidis · 2018
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iNaturalist 2018 competition dataset
iNaturalist 2018 competition dataset · 2018
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Lower bounds for non-convex stochastic optimization
Yossi Arjevani, Yair Carmon, John C Duchi, Dylan J Foster, Nathan Srebro, and Blake Woodworth · 2019
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Momentum-based variance reduction in non-convex sgd
Ashok Cutkosky and Francesco Orabona · 2019
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Decoupling representation and classifier for long-tailed recognition
Bingyi Kang, Saining Xie, Marcus Rohrbach, Zhicheng Yan, Albert Gordo, Jiashi Feng, and Yannis Kalantidis · 2019
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Tilted empirical risk minimization
Tian Li, Ahmad Beirami, Maziar Sanjabi, and Virginia Smith · 2020
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Stochastic recursive gradient descent ascent for stochastic nonconvex-strongly-concave minimax problems
Luo Luo, Haishan Ye, Zhichao Huang, and Tong Zhang · 2020
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A simple and effective framework for pairwise deep metric learning
Qi Qi, Yan Yan, Zixuan Wu, Xiaoyu Wang, and Tianbao Yang · 2020
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Hybrid variance-reduced sgd algorithms for minimax problems with nonconvex-linear function
Quoc Tran-Dinh, Deyi Liu, and Lam M Nguyen · 2020
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Optimal epoch stochastic gradient descent ascent methods for min-max optimization
Yan Yan, Yi Xu, Qihang Lin, Wei Liu, and Tianbao Yang · 2020
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Solving stochastic compositional optimization is nearly as easy as solving stochastic optimization
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Large-scale long-tailed recognition in an open world
Ziwei Liu, Zhongqi Miao, Xiaohang Zhan, Jiayun Wang, Boqing Gong, and Stella X Yu · 2019
Cited alongside, same era.
Distributionally robust optimization: A review
Hamed Rahimian and Sanjay Mehrotra · 2019
Cited alongside, same era.
Distributionally robust optimization and generalization in kernel methods
Matthew Staib and Stefanie Jegelka · 2019
Cited alongside, same era.
Non-asymptotic analysis of stochastic methods for non-smooth non-convex regularized problems
Yi Xu, Rong Jin, and Tianbao Yang · 2019
Cited alongside, same era.
Yan Yan, Yi Xu, Qihang Lin, Lijun Zhang, and Tianbao Yang · 2019
Cited alongside, same era.
A stochastic composite gradient method with incremental variance reduction
Junyu Zhang and Lin Xiao · 2019
Cited alongside, same era.
Momentum schemes with stochastic variance reduction for nonconvex composite optimization
Yi Zhou, Zhe Wang, Kaiyi Ji, Yingbin Liang, and Vahid Tarokh · 2019
Cited alongside, same era.
Tianyi Chen, Yuejiao Sun, and Wotao Yin · 2021
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Learning models with uniform performance via distributionally robust optimization
John C Duchi and Hongseok Namkoong · 2021
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On the bias-variance-cost tradeoff of stochastic optimization
Yifan Hu, Xin Chen, and Niao He · 2021
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Non-convex distributionally robust optimization: Non-asymptotic analysis
Jikai Jin, Bohang Zhang, Haiyang Wang, and Liwei Wang · 2021
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An online method for a class of distributionally robust optimization with non-convex objectives
Qi Qi, Zhishuai Guo, Yi Xu, Rong Jin, and Tianbao Yang · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Weakly-convex–concave min–max optimization: provable algorithms and applications in machine learning
Hassan Rafique, Mingrui Liu, Qihang Lin, and Tianbao Yang · 2021
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Variance reduction via primal-dual accelerated dual averaging for nonsmooth convex finite-sums
Chaobing Song, Stephen J Wright, and Jelena Diakonikolas · 2021
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Sinkhorn distributionally robust optimization
Jie Wang, Rui Gao, and Yao Xie · 2021
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Optimal algorithms for convex nested stochastic composite optimization
Zhe Zhang and Guanghui Lan · 2021
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On the complexity of a practical primal-dual coordinate method
Ahmet Alacaoglu, Volkan Cevher, and Stephen J Wright · 2022
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Cyclip: Cyclic contrastive language-image pretraining
Shashank Goel, Hritik Bansal, Sumit Bhatia, Ryan Rossi, Vishwa Vinay, and Aditya Grover · 2022
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Provable stochastic optimization for global contrastive learning: Small batch does not harm performance
Zhuoning Yuan, Yuexin Wu, Zi-Hao Qiu, Xianzhi Du, Lijun Zhang, Denny Zhou, and Tianbao Yang · 2022
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