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On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms, , и . (27.08.2019)Stochastic Conditional Gradient++: (Non)Convex Minimization and Continuous Submodular Maximization., , , и . SIAM J. Optim., 30 (4): 3315-3344 (2020)MAML and ANIL Provably Learn Representations., , , и . ICML, том 162 из Proceedings of Machine Learning Research, стр. 4238-4310. PMLR, (2022)A Conditional Gradient-based Method for Simple Bilevel Optimization with Convex Lower-level Problem., , , и . AISTATS, том 206 из Proceedings of Machine Learning Research, стр. 10305-10323. PMLR, (2023)An incremental quasi-Newton method with a local superlinear convergence rate., , и . ICASSP, стр. 4039-4043. IEEE, (2017)Large-scale nonconvex stochastic optimization by Doubly Stochastic Successive Convex approximation., , , и . ICASSP, стр. 4701-4705. IEEE, (2017)Decentralized double stochastic averaging gradient., и . ACSSC, стр. 406-410. IEEE, (2015)Doubly stochastic algorithms for large-scale optimization., , и . ACSSC, стр. 1705-1709. IEEE, (2016)FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization., , , , и . AISTATS, том 108 из Proceedings of Machine Learning Research, стр. 2021-2031. PMLR, (2020)A Primal-Dual Quasi-Newton Method for Exact Consensus Optimization., , и . IEEE Trans. Signal Process., 67 (23): 5983-5997 (2019)