From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling | Paper Citation Record

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This paper cites Credibility intervals for the reproduction number of the Covid-19 pandemic using proximal Langevin samplers.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Credibility intervals for the reproduction number of the Covid-19 pandemic using proximal Langevin samplers

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling What regularized auto-encoders learn from the data- generating distribution

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Cor- rection to: convex analysis and monotone operator theory in Hilbert spaces

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Langevin Monte Carlo beyond Lipschitz gradient continuity

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Langevin Monte Carlo and JKO splitting

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This paper cites Alternating proximal-gradient steps for (stochastic) nonconvex-concave minimax problems.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Alternating proximal-gradient steps for (stochastic) nonconvex-concave minimax problems

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This paper cites Sampling from a log- concave distribution with compact support with proximal Langevin Monte Carlo.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Sampling from a log- concave distribution with compact support with proximal Langevin Monte Carlo

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This paper cites The tamed unadjusted Langevin algorithm.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling The tamed unadjusted Langevin algorithm

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Finite-time analysis of projected Langevin Monte Carlo

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling An overview of existing methods and recent advances in sequential monte carlo

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This paper cites Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Convergence of Langevin MCMC in KL-divergence

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Analysis of Langevin Monte Carlo from Poincare to Log-Sobolev

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Score-based diffusion models for accelerated mri

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Diffusion posterior sampling for general noisy inverse problems

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Nonsmooth analysis and optimization

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Plug-and-play split gibbs sam- pler: embedding deep generative priors in bayesian inference

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling User’s guide to viscosity solutions of second order partial differential equations

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This paper cites Optimal scaling results for Moreau-Yosida Metropolis-adjusted Langevin algorithms.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Optimal scaling results for Moreau-Yosida Metropolis-adjusted Langevin algorithms

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling User-friendly guarantees for the langevin monte carlo with inaccurate gradient

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Stochastic model-based minimization of weakly convex functions

Reference 21

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This paper cites Convergence of denoising diffusion models under the manifold hypoth- esis.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Convergence of denoising diffusion models under the manifold hypoth- esis

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Convergence of diffusions and their discretizations: from continuous to discrete processes and back

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Ergodic bsdes under weak dissipative assumptions

Reference 24

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This paper cites Mulog, or how to apply gaussian denoisers to multi-channel sar speckle reduction? IEEE Transactions on Image Processing, 26(9):4389–4403, 2017.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Mulog, or how to apply gaussian denoisers to multi-channel sar speckle reduction? IEEE Transactions on Image Processing, 26(9):4389–4403, 2017

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Markov chains: Basic definitions

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Nonasymptotic convergence analysis for the unadjusted Langevin algorithm

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This paper cites Efficient bayesian computation by prox- imal markov chain monte carlo: when langevin meets moreau.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Efficient bayesian computation by prox- imal markov chain monte carlo: when langevin meets moreau

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This paper cites Analysis of Langevin Monte Carlo via convex optimization.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Analysis of Langevin Monte Carlo via convex optimization

Reference 29

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Reflection couplings and contraction rates for diffusions

Reference 30

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling On the kantorovich–rubinstein theorem

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Tweedie’s formula and selection bias

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Proximal Langevin sam- pling with inexact proximal mapping

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Proximal Interacting Particle Langevin Algorithms

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Pot: Python optimal transport

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This paper cites Covid19 reproduction number: Credibility intervals by blockwise proximal monte carlo samplers.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Covid19 reproduction number: Credibility intervals by blockwise proximal monte carlo samplers

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This paper cites Convexity in ReLU Neural Networks: beyond ICNNs?, 2025.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Convexity in ReLU Neural Networks: beyond ICNNs?, 2025

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This paper cites Stochastic relaxation, gibbs distributions, and the bayesian restoration of images.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Stochastic relaxation, gibbs distributions, and the bayesian restoration of images

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This paper cites Adaptive rejection metropolis sampling within gibbs sampling.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Adaptive rejection metropolis sampling within gibbs sampling

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This paper cites Learning weakly convex regulariz- ers for convergent image-reconstruction algorithms.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Learning weakly convex regulariz- ers for convergent image-reconstruction algorithms

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This paper cites A characterization of proximity operators.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling A characterization of proximity operators

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From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Agem: Solving linear inverse problems via deep priors and sampling

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This paper cites Provable benefit of annealed Langevin Monte Carlo for non-log-concave sampling.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Provable benefit of annealed Langevin Monte Carlo for non-log-concave sampling

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This paper cites Penalized overdamped and under- damped langevin monte carlo algorithms for constrained sampling.Journal of machine learn- ing research, 25(263):1–67, 2024.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Penalized overdamped and under- damped langevin monte carlo algorithms for constrained sampling.Journal of machine learn- ing research, 25(263):1–67, 2024

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This paper cites Convex analysis and minimization al- gorithms I: Fundamentals, volume 305.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Convex analysis and minimization al- gorithms I: Fundamentals, volume 305

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This paper cites On proximal point-type algorithms for weakly convex functions and their connection to the backward euler method.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling On proximal point-type algorithms for weakly convex functions and their connection to the backward euler method

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This paper cites Gradient step denoiser for con- vergent plug-and-play.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Gradient step denoiser for con- vergent plug-and-play

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This paper cites Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Convergent plug-and-play with proximal denoiser and unconstrained regularization parameter

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Observation 0762f55e-eae9-4f8d-b842-6e2b3fcb7fa2 · outbound

This paper cites The performance of the unadjusted langevin algorithm without smoothness assumptions.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling The performance of the unadjusted langevin algorithm without smoothness assumptions

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This paper cites Local convergence of an inexact proximal algorithm for weakly convex functions.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Local convergence of an inexact proximal algorithm for weakly convex functions

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This paper cites Stochastic solutions for linear inverse problems using the prior implicit in a denoiser.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Stochastic solutions for linear inverse problems using the prior implicit in a denoiser

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This paper cites On a space of totally additive functions.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling On a space of totally additive functions

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This paper cites Brownian motion and stochastic calculus, volume 113.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Brownian motion and stochastic calculus, volume 113

Reference 53

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Observation 1f98c139-1c23-44e2-af3a-1d14cbe7c55b · outbound

This paper cites Denoising diffusion restora- tion models.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Denoising diffusion restora- tion models

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:45.047226Z

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Observation 176245d3-31c8-44fc-b30f-8f46b16d059d · outbound

This paper cites Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Accelerated Bayesian imaging by relaxed proximal-point Langevin sampling

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:44.908317Z

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Observation 827c006e-6009-4cc1-a01b-915a82708a98 · outbound

This paper cites Projected stochastic gradient Langevin algorithms for constrained sam- pling and non-convex learning.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Projected stochastic gradient Langevin algorithms for constrained sam- pling and non-convex learning

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:44.721422Z

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Observation c8bd7fcd-12b6-4882-b30d-2128da0a8f4c · outbound

This paper cites A note on the measurability of convex sets.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling A note on the measurability of convex sets

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:44.589871Z

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Observation c3a57596-f712-4530-a3eb-348b3bc708a7 · outbound

This paper cites On maximum a posteriori estimation with plug & play priors and stochastic gradient descent.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling On maximum a posteriori estimation with plug & play priors and stochastic gradient descent

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:44.442835Z

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Observation 31f9643f-7521-4a72-9701-576535f8d03f · outbound

This paper cites Bayesian imaging using Plug & Play Priors: When Langevin meets Tweedie.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Bayesian imaging using Plug & Play Priors: When Langevin meets Tweedie

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:44.263931Z

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Observation 369b553b-cf89-4823-8102-0f4f1e53fe46 · outbound

This paper cites A proximal algorithm for sampling from non-smooth potentials.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling A proximal algorithm for sampling from non-smooth potentials

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:44.130625Z

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Observation a6d54ee8-51e2-4e51-8dbc-3198c1980bfc · outbound

This paper cites Statistics of random processes: I.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Statistics of random processes: I

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:43.936608Z

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Observation 4b89d75d-a90d-41d7-ac02-069d8d98808f · outbound

This paper cites Ergodic bsdes and related pdes with neumann boundary conditions under weak dissipative assumptions.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Ergodic bsdes and related pdes with neumann boundary conditions under weak dissipative assumptions

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:43.788410Z

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Observation 360de103-07c7-44af-bbde-d151ae44c5b5 · outbound

This paper cites Stochastic differential equations and applications.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Stochastic differential equations and applications

Reference 63

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:31.629082Z

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Observation d593562b-913d-4b0b-a916-eb8a76c89336 · outbound

This paper cites A database of human seg- mented natural images and its application to evaluating segmentation algorithms and measur- ing ecological statistics.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling A database of human seg- mented natural images and its application to evaluating segmentation algorithms and measur- ing ecological statistics

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:43.615050Z

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Observation 66889215-f029-44d4-a900-c22e6a4e62a0 · outbound

This paper cites Proximité et dualité dans un espace hilbertien.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Proximité et dualité dans un espace hilbertien

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:43.445762Z

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Observation c12c247d-dcb9-42c9-891c-e60aac3d3730 · outbound

This paper cites Inf-convolution, sous-additivité, convexité des fonctions numériques.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Inf-convolution, sous-additivité, convexité des fonctions numériques

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:43.257872Z

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Observation 9056ff17-3170-4ef2-aa51-f6eb78704c91 · outbound

This paper cites Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Improved bounds for discretization of Langevin diffusions: Near-optimal rates without convexity

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:43.036466Z

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Observation 3122ae84-81c2-48be-9167-e8c4def32a51 · outbound

This paper cites Unadjusted Langevin algorithm for non-convex weakly smooth potentials.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Unadjusted Langevin algorithm for non-convex weakly smooth potentials

Reference 68

Resolution: verified exact; local_arxiv, observed 2026-08-07T15:42:36.203700Z

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Observation ff8c6d49-8133-46fa-96cb-7d09b43a2c1f · outbound

This paper cites Second-order analysis of the moreau–yosida and the lasry–lions regulariza- tions.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Second-order analysis of the moreau–yosida and the lasry–lions regulariza- tions

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:42.810581Z

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Observation 7f27aa08-0a88-4eaf-82ff-b73e2ea5ca7e · outbound

This paper cites Proximal markov chain monte carlo algorithms.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Proximal markov chain monte carlo algorithms

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:42.620407Z

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Observation d71b12cf-5bd3-472a-a658-0ef12a753911 · outbound

This paper cites Learning max- imally monotone operators for image recovery.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Learning max- imally monotone operators for image recovery

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:42.436202Z

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Observation 4c9c7f1f-2d81-421e-871a-1e58c0370a12 · outbound

This paper cites Optimal scaling for the proximal Langevin algorithm in high dimensions.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Optimal scaling for the proximal Langevin algorithm in high dimensions

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:42.279332Z

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Observation 75e19ef3-465b-47ce-a45e-990f32311e5b · outbound

This paper cites A survey on sampling and probe methods for inverse problems.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling A survey on sampling and probe methods for inverse problems

Reference 73

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:42.063573Z

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Observation db45ded1-750f-4e8d-a819-7134059faf76 · outbound

This paper cites On the stability of the stochastic gradient Langevin algo- rithm with dependent data stream.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling On the stability of the stochastic gradient Langevin algo- rithm with dependent data stream

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.913626Z

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Observation 6f0bd429-3a34-4bf1-9fa7-6776b5f307c8 · outbound

This paper cites Plug-and-play posterior sampling under mismatched measurement and prior models.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Plug-and-play posterior sampling under mismatched measurement and prior models

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.769311Z

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Observation 46ab70b3-0356-4cd4-acdd-e6e0d64923c6 · outbound

This paper cites Plug-and-play image restoration with Stochastic deNOising REgularization.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Plug-and-play image restoration with Stochastic deNOising REgularization

Reference 76

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.426997Z

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Observation e5697ede-6f37-46bb-b207-07660e518803 · outbound

This paper cites Convergence analysis of a proximal stochastic denoising regularization algorithm.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Convergence analysis of a proximal stochastic denoising regularization algorithm

Reference 77

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.245874Z

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Observation de776e83-4032-4344-83f2-92f922a728b4 · outbound

This paper cites Langevin diffusions and Metropolis-Hastings algo- rithms.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Langevin diffusions and Metropolis-Hastings algo- rithms

Reference 78

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:41.009687Z

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Observation e769f9b6-760b-4a5d-b558-a38cff60f2e7 · outbound

This paper cites Exponential convergence of Langevin distributions and their discrete approximations.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Exponential convergence of Langevin distributions and their discrete approximations

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.814229Z

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Observation 31c6677b-fd9e-4f9d-b607-cb8b55235ef1 · outbound

This paper cites Variational analysis, volume 317.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Variational analysis, volume 317

Reference 80

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.597608Z

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Observation caee3a42-b886-4ad0-abf2-96ca200ed7c6 · outbound

This paper cites The little engine that could: Regular- ization by denoising (red).

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling The little engine that could: Regular- ization by denoising (red)

Reference 81

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.407632Z

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Observation 3ae133dc-59bb-4eb5-bbb5-d4fead3bc909 · outbound

This paper cites Solving linear inverse problems provably via posterior sampling with latent dif- fusion models.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Solving linear inverse problems provably via posterior sampling with latent dif- fusion models

Reference 82

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.223576Z

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Observation 045dad40-bb4d-4734-a6e6-f848c93e8e11 · outbound

This paper cites Nonlinear total variation based noise removal algorithms.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Nonlinear total variation based noise removal algorithms

Reference 83

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:33.853769Z

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Observation 387f761e-afe2-46a6-b0af-9da608a84a62 · outbound

This paper cites Primal dual interpretation of the proximal stochastic gradient langevin algorithm.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Primal dual interpretation of the proximal stochastic gradient langevin algorithm

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:40.062369Z

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Observation 500f4544-cae1-4a95-ad66-aa36b9973665 · outbound

This paper cites Stochastic proximal Langevin algorithm: Potential splitting and nonasymptotic rates.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Stochastic proximal Langevin algorithm: Potential splitting and nonasymptotic rates

Reference 85

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:39.919314Z

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Observation 4346d93f-7d35-494b-b064-87441935b854 · outbound

This paper cites Weakly convex regularisers for inverse problems: Convergence of critical points and primal- dual optimisation.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Weakly convex regularisers for inverse problems: Convergence of critical points and primal- dual optimisation

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:39.706728Z

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Observation db1bad5c-fd40-47aa-839d-1a06d1bf0a92 · outbound

This paper cites Generative modeling by estimating gradients of the data distribution.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Generative modeling by estimating gradients of the data distribution

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:34.206687Z

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Observation e8873d73-98d2-43cb-b8dc-53cd00793f60 · outbound

This paper cites Bayesian inference in thresh- old models using gibbs sampling.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Bayesian inference in thresh- old models using gibbs sampling

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:39.511497Z

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Observation fb471c78-7222-44e2-bcb8-3f0f70aa62a6 · outbound

This paper cites Provable prob- abilistic imaging using score-based generative priors.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Provable prob- abilistic imaging using score-based generative priors

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:39.345751Z

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Observation 3cf58dec-ad68-4220-b804-a9aca53b184b · outbound

This paper cites Deepinverse: A deep learning frame- work for inverse problems in imaging.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Deepinverse: A deep learning frame- work for inverse problems in imaging

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:39.150656Z

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Observation 189d053b-a94b-463f-be0f-e110cccfadfd · outbound

This paper cites Pareto smoothed importance sampling.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Pareto smoothed importance sampling

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:38.950982Z

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Observation 5bfd22ad-a24a-49fd-9649-1bdfb6964845 · outbound

This paper cites Plug-and-play priors for model based reconstruction.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Plug-and-play priors for model based reconstruction

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:38.797701Z

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Observation 6974f655-a36e-48db-b192-3f8aa20c6763 · outbound

This paper cites Optimal transport: old and new, volume 338.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Optimal transport: old and new, volume 338

Reference 93

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:34.773021Z

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Observation 4b53b125-f4b7-46d0-b13f-cf47dd1c9e0b · outbound

This paper cites Split-and-augmented gibbs sam- pler—application to large-scale inference problems.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Split-and-augmented gibbs sam- pler—application to large-scale inference problems

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:38.597392Z

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Observation ef34c8b5-3339-4c2e-beeb-7d5846a60022 · outbound

This paper cites Image quality assess- ment: from error visibility to structural similarity.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Image quality assess- ment: from error visibility to structural similarity

Reference 95

Resolution: unresolved; no resolver link, observed 2026-08-07T15:42:34.927118Z

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Observation 62dac339-bc99-421c-965f-26ce248f841b · outbound

This paper cites Learning pseudo-contractive denoisers for inverse problems.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Learning pseudo-contractive denoisers for inverse problems

Reference 96

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:38.416516Z

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Observation ddf651f5-0bd8-4b85-86a2-1642bf6dc94d · outbound

This paper cites Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Sampling as optimization in the space of measures: The Langevin dynamics as a composite optimization problem

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:38.208034Z

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Observation af4ba61f-70f4-4e9b-ab90-4be21a37b95c · outbound

This paper cites Convergence of the inexact Langevin algorithm and score-based generative models in KL divergence.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Convergence of the inexact Langevin algorithm and score-based generative models in KL divergence

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:37.956862Z

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Observation e9c34f65-21b6-4a4d-9dc6-4a9d4b9af066 · outbound

This paper cites Plug- and-play image restoration with deep denoiser prior.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling Plug- and-play image restoration with deep denoiser prior

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T15:42:37.774874Z

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Observation b5bf42b6-4bbe-41a0-8660-fd0b32352503 · outbound

This paper cites The unrea- sonable effectiveness of deep features as a perceptual metric.

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling The unrea- sonable effectiveness of deep features as a perceptual metric

Reference 100

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