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Tuning hyperparameters is a crucial but arduous part of the machine learning pipeline.
Discrete Event Systems: Sensitivity Analysis and Stochastic Optimization by the Score Function Method
Reuven Y. Rubinstein and Alexander Shapiro · 1993
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Exponentiated gradient versus gradient descent for linear predictors
Jyrki Kivinen and Manfred K. Warmuth · 1997
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Adaptive online gradient descent
Peter L. Bartlett, Elad Hazan, and Alexander Rakhlin · 2008
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Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger H. Hoos, and Kevin Leyton-Brown · 2011
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Online learning and online convex optimization
Shai Shalev-Shwartz · 2011
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Random search for hyper-parameter optimization
James Bergstra and Yoshua Bengio · 2012
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Practical bayesian optimization of machine learning algorithms
Jasper Snoek, Hugo Larochelle, and Ryan P. Adams · 2012
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Adam: A method for stochastic optimization
Diederik P. Kingma and Jimmy Ba · 2015
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Model-agnostic meta-learning for fast adaptation of deep networks
Chelsea Finn, Pieter Abbeel, and Sergey Levine · 2017
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A PAC approach to application-specific algorithm selection
Rishi Gupta and Tim Roughgarden · 2017
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Communication-efficient learning of deep networks from decentralized data
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
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LEAF: A benchmark for federated settings
Sebastian Caldas, Peter Wu, Tian Li, Jakub Konečný, H. Brendan McMahan, Virginia Smith, and Ameet Talwalkar · 2018
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Federated meta-learning for recommendation
Fei Chen, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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Learning rate adaptation for federated and differentially private learning
Antti Koskela and Antti Honkela · 2018
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Hyperband: A novel bandit-based approach to hyperparameter optimization
Liam Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2018
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Learning differentially private recurrent language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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On first-order meta-learning algorithms
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Efficient neural architecture search via parameter sharing
Hieu Pham, Melody Y. Guan, Barret Zoph, Quoc V. Le, and Jeff Dean · 2018
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Searching for a robust neural architecture in four GPU hours
Xuanyi Dong and Yi Yang · 2019
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Semi-cyclic stochastic gradient descent
Hubert Eichner, Tomer Koren, H. Brendan McMahan, Nathan Srebro, and Kunal Talwar · 2019
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Meta-learning of neural architectures for few-shot learning
Thomas Elsken, Benedikt Staffler, Jan Hendrik Metzen, and Frank Hutter · 2019
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An efficient framework for clustered federated learning
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Towards non-i.i.d. and invisible data with FedNAS: Federated deep learning via neural architecture search
Chaoyang He, Murali Annavaram, and Salman Avestimehr · 2020
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SCAFFOLD: Stochastic controlled averaging for federated learning
Sai Praneeth Karimireddy, Satyen Kale, Mehryar Mohri, Sashank J. Reddi, Sebastian U. Stich, and Ananda Theertha Suresh · 2020
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Differentially private meta-learning
Jeffrey Li, Mikhail Khodak, Sebastian Caldas, and Ameet Talwalkar · 2020
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Ditto: Fair and robust federated learning through personalization
Tian Li, Shengyuan Hu, Ahmad Beirami, and Virginia Smith · 2020
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Federated learning: Challenges, methods, and future directions
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Improving federated learning personalization via model agnostic meta learning
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Advances and open problems in federated learning
Peter Kairouz, H. Brendan McMahan, Brendan Avent, Aurélien Bellet, Mehdi Bennis, Arjun Nitin Bhagoji, Keith Bonawitz, Zachary Charles, Graham Cormode, Rachel Cummings, Rafael G. L. D’Oliveira, Salim El Rouayheb, David Evans, Josh Gardner, Zachary Garrett, Adrià Gascón, Badih Ghazi, Phillip B. Gibbons, Marco Gruteser, Zaid Harchaoui, Chaoyang He, Lie He, Zhouyuan Huo, Ben Hutchinson, Justin Hsu, Martin Jaggi, Tara Javidi, Gauri Joshi, Mikhail Khodak, Jakub Konečný, Aleksandra Korolova, Farinaz Koushanfar, Sanmi Koyejo, Tancrède Lepoint, Yang Liu, Prateek Mittal, Mehryar Mohri, Richard Nock, Ayfer Özgür, Rasmus Pagh, Mariana Raykova, Hang Qi, Daniel Ramage, Ramesh Raskar, Dawn Song, Weikang Song, Sebastian U. Stich, Ziteng Sun, Ananda Theertha Suresh, Florian Tramèr, Praneeth Vepakomma, Jianyu Wang, Li Xiong, Zheng Xu, Qiang Yang, Felix X. Yu, Han Yu, and Sen Zhao · 2019
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Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2019
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Provable guarantees for gradient-based meta-learning
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar · 2019
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StacNAS: Towards stable and consistent differentiable neural architecture search
Guilin Li, Xing Zhang, Zitong Wang, Zhenguo Li, and Tong Zhang · 2019
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Random search and reproducibility for neural architecture search
Liam Li and Ameet Talwalkar · 2019
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Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Federated optimization in heterogeneous networks
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Fair resource allocation in federated learning
Tian Li, Maziar Sanjabi, Ahmad Beirami, and Virginia Smith · 2020
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Towards fast adaptation of neural architectures with meta-learning
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Three approaches for personalization with applications to federated learning
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Federated neural architecture search
Mengwei Xu, Yuxin Zhao, Kaigui Bian, Gang Huang, Qiaozhu Mei, and Xuanzhe Liu · 2020
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Salvaging federated learning by local adaptation
Tao Yu, Eugene Bagdasaryan, and Vitaly Shmatikov · 2020
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Federated learning based on dynamic regularization
Durmus Alp Emre Acar, Yue Zhao, Ramon Matas Navarro, Matthew Mattina, Paul N. Whatmough, and Venkatesh Saligrama · 2021
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Federated learning via posterior averaging: A new perspective and practical algorithms
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Geometry-aware gradient algorithms for neural architecture search
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Adaptive federated optimization
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Guarantees for tuning the step size using a learning-to-learn approach
Xiang Wang, Shuai Yuan, Chenwei Wu, and Rong Ge · 2021
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