Heat kernel regularization ensures the regularized Hessian stays asymptotically nondegenerate near nonsmooth minimizers of the form |x|^a, making the continuation equation locally solvable for small t.
Random gradient-free minimization of convex functions.Foundations of Computational Mathematics, 17(2):527–566
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Slowly Annealed Langevin Dynamics provides non-asymptotic KL-based convergence guarantees for tracking moving targets and enables training-free guided generation via a velocity-aware correction that accounts for pretrained marginals.
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From Nonsmooth Minima to Smooth Branches via Heat Kernel Regularization
Heat kernel regularization ensures the regularized Hessian stays asymptotically nondegenerate near nonsmooth minimizers of the form |x|^a, making the continuation equation locally solvable for small t.
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Slowly Annealed Langevin Dynamics: Theory and Applications to Training-Free Guided Generation
Slowly Annealed Langevin Dynamics provides non-asymptotic KL-based convergence guarantees for tracking moving targets and enables training-free guided generation via a velocity-aware correction that accounts for pretrained marginals.