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This paper presents distributed Riemannian online gradient descent on Hadamard manifolds with curvature-independent regret bounds for horospherical convex functions, achieving rates matching Euclidean optimization.
This thesis tackles seven challenges in distributed and federated optimization, introducing methods like ProxSkip and Variance Reduced ProxSkip, and establishing theoretical foundations for communication-efficient, robust, and practical algorithms.
This paper proposes a data-driven elastic-net support vector machine that learns simplex-constrained weights over candidate pinball losses, with a distributed solver for vertically partitioned high-dimensional data. Theoretical guarantees and experiments demonstrate equivalence to centralized training under common initialization.
This paper proposes F2CTO, the first distributed first-order constrained trilevel optimization method for robust coreset selection over distributed networks, with a non-asymptotic convergence guarantee of O(ε^(-3/2)).