The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.
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DP-Muon adapts matrix-orthogonalized momentum optimization to differential privacy via per-matrix clipping and noise addition, with proofs of inherited privacy and optimization guarantees plus a bias-corrected version that improves private fine-tuning utility.
RSC-ZO achieves high-probability ε-stationary points for stochastic ZO optimization under weak-L_p heavy-tailed noise with Õ(d^{p/2(p-1)} ε^{-(3p-2)/(p-1)}) function queries.
citing papers explorer
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Stochastic Compositional Optimization via Hybrid Momentum Frank--Wolfe
The Hybrid Momentum Stochastic Frank-Wolfe algorithm achieves O(K^{-1/4}) convergence in the generalized Frank-Wolfe gap for non-convex stochastic compositional optimization with Lipschitz outer functions.
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DP-Muon: Differentially Private Optimization via Matrix-Orthogonalized Momentum
DP-Muon adapts matrix-orthogonalized momentum optimization to differential privacy via per-matrix clipping and noise addition, with proofs of inherited privacy and optimization guarantees plus a bias-corrected version that improves private fine-tuning utility.
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Stochastic Zeroth-Order Optimization Under Heavy-Tailed Noise
RSC-ZO achieves high-probability ε-stationary points for stochastic ZO optimization under weak-L_p heavy-tailed noise with Õ(d^{p/2(p-1)} ε^{-(3p-2)/(p-1)}) function queries.