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Stochastic Conditional Gradient++: (Non)Convex Minimization and Continuous Submodular Maximization., , , and . SIAM J. Optim., 30 (4): 3315-3344 (2020)On the Convergence Theory of Gradient-Based Model-Agnostic Meta-Learning Algorithms, , and . (Aug 27, 2019)Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy., , , , , , and . CoRR, (2018)Online optimization in dynamic environments: Improved regret rates for strongly convex problems., , , and . CDC, page 7195-7201. IEEE, (2016)Network Adaptive Federated Learning: Congestion and Lossy Compression., , and . INFOCOM, page 1-10. IEEE, (2023)Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach., and . NeurIPS, page 3824-3835. (2021)Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match., , , and . NeurIPS, page 13066-13076. (2019)Decentralized double stochastic averaging gradient., and . ACSSC, page 406-410. IEEE, (2015)Doubly stochastic algorithms for large-scale optimization., , and . ACSSC, page 1705-1709. IEEE, (2016)An incremental quasi-Newton method with a local superlinear convergence rate., , and . ICASSP, page 4039-4043. IEEE, (2017)