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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A statistical inference framework for day-to-day traffic dynamics models allows identifiability, consistency proofs, and parameter estimation from trajectory data, with extensions for heterogeneity and privacy, validated on simulations and Ann Arbor data.
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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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Statistical Inference of Day-to-Day Traffic Dynamics
A statistical inference framework for day-to-day traffic dynamics models allows identifiability, consistency proofs, and parameter estimation from trajectory data, with extensions for heterogeneity and privacy, validated on simulations and Ann Arbor data.