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Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings., , and . ICML, volume 80 of Proceedings of Machine Learning Research, page 3613-3622. PMLR, (2018)Decentralized Quasi-Newton Methods., , and . IEEE Trans. Signal Process., 65 (10): 2613-2628 (2017)Provably Private Distributed Averaging Consensus: An Information-Theoretic Approach., , , and . IEEE Trans. Inf. Theory, 69 (11): 7317-7335 (November 2023)Sharpened Quasi-Newton Methods: Faster Superlinear Rate and Larger Local Convergence Neighborhood., , , and . ICML, volume 162 of Proceedings of Machine Learning Research, page 10228-10250. PMLR, (2022)Submodular Meta-Learning., , and . NeurIPS, (2020)A data-driven approach to stochastic network optimization., , , , and . GlobalSIP, page 510-514. IEEE, (2016)Efficient Distributed Hessian Free Algorithm for Large-scale Empirical Risk Minimization via Accumulating Sample Strategy., , , , , , and . AISTATS, volume 108 of Proceedings of Machine Learning Research, page 2634-2644. PMLR, (2020)Network Newton Distributed Optimization Methods., , and . IEEE Trans. Signal Process., 65 (1): 146-161 (2017)Statistical and Computational Complexities of BFGS Quasi-Newton Method for Generalized Linear Models., , , and . Trans. Mach. Learn. Res., (2024)Decentralized double stochastic averaging gradient., and . ACSSC, page 406-410. IEEE, (2015)