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Score matching is an approach to learning probability distributions parametrized up to a constant of proportionality (e.g.
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Diffusions, Markov processes and martingales: Volume 2, Itô calculus , volume 2
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Asymptotic statistics , volume 3
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Markov chain decomposition for convergence rate analysis
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A note on the poincaré inequality for convex domains
Mario Bebendorf · 2003
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Convex optimization
Stephen P Boyd and Lieven Vandenberghe · 2004
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Parallel tempering: Theory, applications, and new perspectives
David J Earl and Michael W Deem · 2005
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Estimation of non-normalized statistical models by score matching
Aapo Hyvärinen · 2005
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Diffusions hypercontractives
Dominique Bakry and Michel Émery · 2006
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A new criterion for the logarithmic sobolev inequality and two applications
Felix Otto and Maria G Reznikoff · 2007
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A general two-scale criteria for logarithmic sobolev inequalities
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Simulated tempering langevin monte carlo ii: An improved proof using soft markov chain decomposition
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Beyond log-concavity: Provable guarantees for sampling multi-modal distributions using simulated tempering langevin monte carlo
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Minimum stein discrepancy estimators
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Optimal convergence rate of hamiltonian monte carlo for strongly logconcave distributions
Zongchen Chen and Santosh S Vempala · 2019
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Polynomial learning of distribution families
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Riemann manifold langevin and hamiltonian monte carlo methods
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Operator reverse monotonicity of the inverse
Alexis Akira Toda · 2011
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Interpretation and generalization of score matching
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Sequential markov chain monte carlo
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Fast convergence for langevin diffusion with manifold structure
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Score-based generative modeling through stochastic differential equations
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Dimension-free log-sobolev inequalities for mixture distributions
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Score-based generative modeling with critically-damped langevin diffusion
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Estimating high order gradients of the data distribution by denoising
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Robustly learning mixtures of k arbitrary gaussians
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Hongrui Chen, Holden Lee, and Jianfeng Lu · 2022
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Statistical efficiency of score matching: The view from isoperimetry
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Convergence for score-based generative modeling with polynomial complexity
Holden Lee, Jianfeng Lu, and Yixin Tan · 2022
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On explicit l 2-convergence rate estimate for underdamped langevin dynamics
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Convergence of score-based generative modeling for general data distributions
Holden Lee, Jianfeng Lu, and Yixin Tan · 2023
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Provable benefits of score matching
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