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Generalization performance is a key metric in evaluating machine learning models when applied to real-world applications.
Olivier Bousquet and André Elisseeff. Stability and generalization. The Journal of Machine Learning Research 2: 499-526, 2002
2002
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Shai Shalev-Shwartz, Ohad Shamir, Nathan Srebro, and Karthik Sridharan. Learnability, stability and uniform convergence. The Journal of Machine Learning Research , 11(90): 2635-2670, 2010
2010
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Jeffrey Dean et al. Large scale distributed deep networks. Advances in Neural Information Processing Systems , 25, 2012
2012
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Li Deng. The mnist database of handwritten digit images for machine learning research. IEEE Signal Processing Magazine , 29(6), 141–142, 2012
2012
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Aaron Defazio, Francis Bach, and Simon Lacoste-Julien. SAGA: A fast incremental gradient method with support for non-strongly convex composite objectives. Advances in Neural Information Processing Systems , 27, 2014
2014
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2015
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2016
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Moritz Hardt, Ben Recht, and Yoram Singer. Train faster, generalize better: Stability of stochastic gradient descent. International Conference on Machine Learning , PMLR 48:1225-1234, 2016
2016
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Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, Blaise Aguera y Arcas. Communication-efficient learning of deep networks from decentralized data. International Conference on Artificial Intelligence and Statistics , PMLR 54:1273-1282, 2017
2017
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2018
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2018
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Mehryar Mohri,Afshin Rostamizadeh,and Ameet Talwalkar. Foundations of Machine Learning . MIT Press, second edition, 2018
2018
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Ilja Kuzborskij and Christoph Lampert. Data-dependent stability of stochastic gradient descent. International Conference on Machine Learning , PMLR 80:2815-2824, 2018
2018
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Wenlong Mou, Liwei Wang, Xiyu Zhai, and Kai Zheng. Generalization bounds of SGLD for non-convex learning: Two theoretical viewpoints. Conference On Learning Theory , PMLR 75:605-638, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Mehryar Mohri, Gary Sivek, and Ananda Theertha Suresh. Agnostic federated learning. International Conference on Machine Learning , PMLR pp. 4615-4625, 2019
2019
Cited alongside, same era.
Honglin Yuan, Manzil Zaheer, and Sashank Reddi. Federated composite optimization. Proceedings of the 38th International Conference on Machine Learning . PMLR 139:12253-12266, 2021
2021
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2021
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Alireza Fallah, Aryan Mokhtari, and Asuman Ozdaglar. Generalization of model-agnostic meta-learning algorithms: Recurring and unseen tasks. Advances in Neural Information Processing Systems . 34, 2021
2021
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2021
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Tian Li, Anit Kumar Sahu, Manzil Zaheer, Maziar Sanjabi, Ameet Talwalkar, and Virginia Smithy. FedDANE: A federated newton-type method. 53rd Asilomar Conference on Signals, Systems, and Computers . pp. 1227-1231, 2019
2019
Cited alongside, same era.
Dong Yin, Ramchandran Kannan, and Peter Bartlett. Rademacher complexity for adversarially robust generalization. International Conference on Machine Learning , PMLR 97:7085-7094, 2019
2019
Cited alongside, same era.
Idan Attias, Aryeh Kontorovich, and Yishay Mansour. Improved generalization bounds for robust learning. International Conference on Algorithmic Learning Theory , 98:162-183. PMLR, 2019
2019
Cited alongside, same era.
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank Reddi, Sebastian Stich, and Ananda Theertha Suresh. Scaffold: Stochastic controlled averaging for federated learning. International Conference on Machine Learning , PMLR 119:5132–5143, 2020
2020
Cited alongside, same era.
Honglin Yuan and Tengyu Ma. Federated accelerated stochastic gradient descent. Advances in Neural Information Processing Systems . 33, 2020
2020
Cited alongside, same era.
Jianyu Wang, Qinghua Liu, Hao Liang, Gauri Joshi, and H. Vincent Poor. Tackling the objective inconsistency problem in heterogeneous federated optimization. Advances in Neural Information Processing Systems , 33, 2020
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Reese Pathak and Martin J. Wainwright. FedSplit: an algorithmic framework for fast federated optimization. Advances in Neural Information Processing Systems , 33, 2020
2020
Cited alongside, same era.
Aritra Mitra, Rayana Jaafar, George J. Pappas, and Hamed Hassani. Linear convergence in federated learning: tackling client heterogeneity and sparse gradients. Advances in Neural Information Processing Systems , 34, 2021
2021
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2021
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Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals. Understanding deep learning (still) requires rethinking generalization. Communications of the ACM , 64(3): 107-115, 2021
2021
Later among the works it cites.
Xiaotong Yuan and Ping Li. On convergence of FedProx: Local dissimilarity invariant Bounds, non-smoothness and beyond. Advances in Neural Information Processing Systems . 35, 2022
2022
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2022
Later among the works it cites.
Xiaolin Hu, Shaojie Li,and Yong Liu. Generalization bounds for federated learning: fast rates, unparticipating clients and unbounded losses. Accepted by ICLR , 2023
2023
Closest in time.
Xiaochun Niu and Ermin Wei. FedHybrid: A hybrid federated optimization method for heterogeneous clients. IEEE Transactions on Signal Processing , 71:150-163, 2023
2023
Closest in time.
Tsing, Yuwen Yang, and NewAlexandria. TsingZ0/PFL-Non-IID: First Release (v0.1.0). Zenodo. https://doi.org/10.5281/zenodo.7780680, 2023
2023
Closest in time.