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Parameter-transfer is a well-known and versatile approach for meta-learning, with applications including few-shot learning, federated learning, and reinforcement learning.
When relaxations go bad: "differentially-private" machine learning, 2019
Bargav Jayaraman and David Evans · 1902
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Learning-to-learn stochastic gradient descent with biased regularization, 2019
Giulia Denevi, Carlo Ciliberto, Riccardo Grazzi, and Massimiliano Pontil · 1903
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Xi Sheryl Zhang, Fengyi Tang, Hiroko Dodge, Jiayu Zhou, and Fei Wang · 1905
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Private stochastic convex optimization with optimal rates
Raef Bassily, Vitaly Feldman, Kunal Talwar, and Abhradeep Thakurta · 1908
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Federated learning: Challenges, methods, and future directions, 2019
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 1908
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Credit card fraud detection using meta-learning: Issues and initial results 1
Salvatore J. Stolfo, David W. Fan, Wenke Lee, Andreas L. Prodromidis, and Philip K. Chan · 1997
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A model of inductive bias learning
Jonathan Baxter · 2000
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On the generalization ability of on-line learning algorithms
Nicoló Cesa-Bianchi, Alex Conconi, and Claudio Gentile · 2004
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One shot learning of simple visual concepts
Brenden M. Lake, Ruslan Salakhutdinov, Jason Gross, and Joshua B. Tenenbaum · 2011
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The algorithmic foundations of differential privacy
Cynthia Dwork and Aaron Roth · 2014
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Understanding Machine Learning: From Theory to Algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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Model inversion attacks that exploit confidence information and basic countermeasures
Matt Fredrikson, Somesh Jha, and Thomas Ristenpart · 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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Communication-efficient learning of deep networks from decentralized data
H Brendan McMahan, Eider Moore, Daniel Ramage, Seth Hampson, and Blaise Aguera y Arcas · 2017
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Optimization as a model for few-shot learning
Sachin Ravi and Hugo Larochelle · 2017
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Membership inference attacks against machine learning models
Reza Shokri, Marco Stronati, and Vitaly Shmatikov · 2017
Federated meta-learning for recommendation
Fei Chen, Zhenhua Dong, Zhenguo Li, and Xiuqiang He · 2018
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Minimax optimal procedures for locally private estimation
John Duchi, Martin Wainwright, and Michael Jordan · 2018
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Differentially private federated learning: A client level perspective, 2018
Robin C. Geyer, Tassilo J. Klein, and Moin Nabi · 2018
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Learning differentially private language models
H. Brendan McMahan, Daniel Ramage, Kunal Talwar, and Li Zhang · 2018
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On first-order meta-learning algorithms
Alex Nichol, Joshua Achiam, and John Schulman · 2018
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Federated multi-task learning
Virginia Smith, Chao-Kai Chiang, Maziar Sanjabi, and Ameet Talwalkar · 2017
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cpsgd: Communication-efficient and differentially-private distributed sgd
Naman Agarwal, Ananda Theertha Suresh, Felix Xinnan X Yu, Sanjiv Kumar, and Brendan McMahan · 2018
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LEAF: A benchmark for federated settings, 2018
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Provable guarantees for gradient-based meta-learning
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar
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Adaptive gradient-based meta-learning methods
Mikhail Khodak, Maria-Florina Balcan, and Ameet Talwalkar
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A theoretical analysis of contrastive unsupervised representation learning
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Protection against reconstruction and its applications in private federated learning, 2019
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