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We propose a Monte Carlo sampler from the reverse diffusion process.
Logarithmic sobolev inequalities
Leonard Gross · 1975
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Diffusion on compact riemannian manifolds and logarithmic sobolev inequalities
OS Rothaus · 1981
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Probabilistic inference using Markov chain Monte Carlo methods
Radford M Neal · 1993
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The markov chain monte carlo method: an approach to approximate counting and integration
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Monte Carlo statistical methods , volume 2
Christian P Robert, George Casella, and George Casella · 1999
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Analysis and geometry of Markov diffusion operators , volume 103
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Georg Menz and André Schlichting · 2014
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Lower bounds for the gibbs sampler over mixtures of gaussians
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Maxim Raginsky, Alexander Rakhlin, and Matus Telgarsky · 2017
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The sample size required in importance sampling
Sourav Chatterjee and Persi Diaconis · 2018
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Xiang Cheng and Peter Bartlett · 2018
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Underdamped langevin mcmc: A non-asymptotic analysis
Xiang Cheng, Niladri S Chatterji, Peter L Bartlett, and Michael I Jordan · 2018
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Raaz Dwivedi, Yuansi Chen, Martin J Wainwright, and Bin Yu · 2018
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Alain Durmus and Eric Moulines · 2019
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Sampling can be faster than optimization
Yi-An Ma, Yuansi Chen, Chi Jin, Nicolas Flammarion, and Michael I Jordan · 2019
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Poincaré and log–sobolev inequalities for mixtures
André Schlichting · 2019
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Theoretical guarantees for sampling and inference in generative models with latent diffusions
Belinda Tzen and Maxim Raginsky · 2019
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High-order langevin diffusion yields an accelerated mcmc algorithm
Wenlong Mou, Yi-An Ma, Martin J Wainwright, Peter L Bartlett, and Michael I Jordan · 2021
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Sampling from the sherrington-kirkpatrick gibbs measure via algorithmic stochastic localization
Ahmed El Alaoui, Andrea Montanari, and Mark Sellke · 2022
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Analysis of high-dimensional distributions using pathwise methods
Ronen Eldan · 2022
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