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New preprint introducing SoftServe, a quasi-Newton optimization method designed for non-convex objectives that scales to very large neural networks, addressing the practicality of quasi-Newton methods in modern deep learning.
This paper proposes FedCoMuon, a federated compositional Muon optimizer for matrix-wise models, along with a variance-reduced variant (FedCoMuon-VR). The authors provide convergence analysis under non-i.i.d. and non-convex settings, showing improved sample complexity over existing FedMuon algorithms, and demonstrate competitive performance on robust federated learning and task-distributed risk-sensitive meta learning.
This paper introduces a curvature-adaptive Follow-the-Perturbed-Leader (FTPL) algorithm for online optimization that achieves optimal regret bounds for both non-convex Lipschitz losses and strongly convex losses, using a time-varying perturbation scale.