Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions | Paper Citation Record

Observation 3b10320c-22e9-48bd-b2de-e6277adc9a14 · inbound

Low-dimensional adaptation of diffusion models: Convergence in total variation cites this paper.

Low-dimensional adaptation of diffusion models: Convergence in total variation Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 11

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Observation d85c5efb-ac2e-46b9-9d36-497298d1e1e9 · inbound

Memorization and Regularization in Generative Diffusion Models cites this paper.

Memorization and Regularization in Generative Diffusion Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 9

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Observation 533073ab-b585-47ed-b42e-a10b9e376023 · inbound

Adaptivity and Convergence of Probability Flow ODEs in Diffusion Generative Models cites this paper.

Adaptivity and Convergence of Probability Flow ODEs in Diffusion Generative Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 8

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Observation 8908ba33-e341-4015-9331-03746b77f918 · inbound

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning cites this paper.

Score as Action: Fine-Tuning Diffusion Generative Models by Continuous-time Reinforcement Learning Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 3

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Observation 36210e9c-474f-4821-bc87-b64dc8ae21bd · inbound

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? cites this paper.

Can Diffusion Models Learn Hidden Inter-Feature Rules Behind Images? Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2023

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Observation bf0c8c8d-8adb-47da-990f-5e8fa11e5dae · inbound

Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration cites this paper.

Advancing Wasserstein Convergence Analysis of Score-Based Models: Insights from Discretization and Second-Order Acceleration Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 6

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Observation 31d6ac62-5404-4eb9-97ba-e7d3277254de · inbound

From stability of Langevin diffusion to convergence of proximal MCMC for non-log-concave sampling cites this paper.

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

Reference 11

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Observation 9c9ba75f-774d-471f-a416-b06b2c27c3f2 · inbound

Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage cites this paper.

Importance Weighted Score Matching for Diffusion Samplers with Enhanced Mode Coverage Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 17

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Observation 83c82a94-01dd-4689-b5a7-010318908f37 · inbound

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models cites this paper.

Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2025

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Observation 1e86bae1-ebd7-44d9-9cbe-a962ca9ace3d · inbound

Efficient Controllable Diffusion via Optimal Classifier Guidance cites this paper.

Efficient Controllable Diffusion via Optimal Classifier Guidance Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 7

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Observation 9f563c67-1081-4163-8edd-3a87084d3389 · inbound

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis cites this paper.

Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2021

Resolution: unresolved; no resolver link, observed 2026-08-07T12:06:37.973209Z

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Observation b7818177-989d-4587-ace0-0a44dcc43031 · inbound

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach cites this paper.

Solving Inverse Problems via Diffusion-Based Priors: An Approximation-Free Ensemble Sampling Approach Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 114

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Observation 648a7b7f-c240-4dc0-8508-202b85cbd125 · inbound

Diffusion models under low-noise regime cites this paper.

Diffusion models under low-noise regime Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 31

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Observation a1b1cf43-94ca-4b46-bd77-9c4519c3ffc7 · inbound

Ambient Diffusion Omni: Training Good Models with Bad Data cites this paper.

Ambient Diffusion Omni: Training Good Models with Bad Data Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 9

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Observation 0ec3ca26-b525-4a95-bfe0-5761ebd1f829 · inbound

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models cites this paper.

Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 13

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Observation bcfbcda6-8b9d-4316-bd78-822702fddcb7 · inbound

Faster Diffusion Models via Higher-Order Approximation cites this paper.

Faster Diffusion Models via Higher-Order Approximation Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 7

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Observation 34fb8965-2aa1-4790-b89f-42495f310844 · inbound

Generalization bounds for score-based generative models: a synthetic proof cites this paper.

Generalization bounds for score-based generative models: a synthetic proof Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2025

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Observation 72ba139d-ae2b-48ab-ab35-9406d0f58ebc · inbound

When and how can inexact generative models still sample from the data manifold? cites this paper.

When and how can inexact generative models still sample from the data manifold? Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 15

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Observation 2f8c25c1-9c1d-476d-a7c3-32f96852f03d · inbound

Non-asymptotic convergence bound of conditional diffusion models cites this paper.

Non-asymptotic convergence bound of conditional diffusion models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 36

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Observation e33d60f2-bc06-467d-ab08-cc84bacf38ac · inbound

Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors cites this paper.

Blade: A Derivative-free Bayesian Inversion Method using Diffusion Priors Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2020

Resolution: unresolved; no resolver link, observed 2026-08-04T10:17:46.510580Z

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Observation ec270d48-8962-4d7b-a392-baf6e2216e2e · inbound

Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants cites this paper.

Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-16T23:03:39.130798Z

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Observation 3e971bae-c4d0-4c8c-86c0-53c3352679a9 · inbound

A Gaussian Perspective for Distributional Discrepancy in Generative Diffusion Models cites this paper.

A Gaussian Perspective for Distributional Discrepancy in Generative Diffusion Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 26

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Observation 944bbdea-50dd-4736-b13b-72807d10768d · inbound

Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity cites this paper.

Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-15T06:45:11.118893Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 428f7453-a6d0-4a13-a009-66c4fd9c08d6 · inbound

Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2 cites this paper.

Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2 Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-12T00:46:13.324191Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 618493a3-2183-4428-b6a0-0bbc902156f5 · inbound

Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2 cites this paper.

Potential Hessian Ascent III: Sampling the Sherrington--Kirkpatrick Model at Beta < 1/2 Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 36

Resolution: verified exact; arxiv_id, observed 2026-05-20T23:49:15.432073Z

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Observation 8e02bf32-9819-4301-8c70-5fab9a82d2ce · inbound

Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective cites this paper.

Energy Generative Modeling: A Lyapunov-based Energy Matching Perspective Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 5

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:36:08.254255Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 8b6b622c-bc78-4af1-b6b6-f67732c3e3b4 · inbound

Decentralized Diffusion Policy Learning for Enhanced Exploration in Cooperative Multi-agent Reinforcement Learning cites this paper.

Decentralized Diffusion Policy Learning for Enhanced Exploration in Cooperative Multi-agent Reinforcement Learning Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-05-11T04:45:55.059880Z

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Observation 110cb9c7-9d9e-42fe-b504-45c8a64e2a19 · inbound

Proximal-Based Generative Modeling for Bayesian Inverse Problems cites this paper.

Proximal-Based Generative Modeling for Bayesian Inverse Problems Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 26

Resolution: verified exact; arxiv_id, observed 2026-05-14T17:57:33.450923Z

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No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation b96a3a52-f2ee-47a5-aaf5-10fe78dec4c1 · inbound

Training-Free Generative Sampling via Moment-Matched Score Smoothing cites this paper.

Training-Free Generative Sampling via Moment-Matched Score Smoothing Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 21

Resolution: verified exact; arxiv_id, observed 2026-05-15T02:33:32.424884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation e0e78169-14a2-4ea2-a912-d0428e52fd76 · inbound

Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures cites this paper.

Dimension-Uniform Discretization Analysis of Preconditioned Annealed Langevin Dynamics for Multimodal Gaussian Mixtures Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-05-19T22:02:48.976102Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation df1475c2-4e31-48fd-a6d1-36ce5a629735 · inbound

The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler cites this paper.

The Value of Covariance Matching in Gaussian DDPMs and the Lanczos Sampler Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2

Resolution: verified exact; arxiv_id, observed 2026-05-22T07:41:14.867460Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a9ad830e-2180-4230-986a-17bada2317d2 · inbound

Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning cites this paper.

Multi-Objective Learning for Diffusion Models: A Statistical Theory under Semi-Supervised Learning Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 1

Resolution: verified exact; arxiv_id, observed 2026-06-30T12:34:39.381441Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0562463e-4b4f-4762-bc1c-c1b835b4e8fd · inbound

Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory cites this paper.

Guided Flow Matching for Forward and Inverse PDE Problems with Sparse Observations: Algorithm and Theory Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-06-29T20:53:58.039806Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation a7398fd1-2229-4e3b-ae94-5ee5818cfec4 · inbound

Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization cites this paper.

Scaling World-Model Reinforcement Learning Through Diffusion Policy Optimization Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 56

Resolution: verified exact; arxiv_id, observed 2026-06-29T22:54:01.427101Z

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Observation ee50d856-1104-4398-9034-1cd8951909d9 · inbound

Smoothed Score Queries and the Complexity of Sampling cites this paper.

Smoothed Score Queries and the Complexity of Sampling Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-06-29T14:53:31.380658Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 9f41ea35-7174-454f-b28f-95b39b47a2ce · inbound

Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence cites this paper.

Global Convergence of Gradient Descent for Score Matching in Gaussian Mixtures via Reverse Fisher Divergence Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 2

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Observation f7cb9f80-0c5b-4499-ae50-c9ef5979b312 · inbound

Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices cites this paper.

Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 12

Resolution: verified exact; arxiv_id, observed 2026-07-04T12:49:52.467344Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 1a306708-0f29-4f31-8c72-beaf5ab21d7f · inbound

A Variational-Flow Analysis of Diffusion-Based Speech Enhancement under Noise-Power Mismatch cites this paper.

A Variational-Flow Analysis of Diffusion-Based Speech Enhancement under Noise-Power Mismatch Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-04T17:40:00.956916Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 641499a4-2d42-495a-9834-6106693124df · inbound

A Variational-Flow Analysis of Diffusion-Based Speech Enhancement under Noise-Power Mismatch cites this paper.

A Variational-Flow Analysis of Diffusion-Based Speech Enhancement under Noise-Power Mismatch Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 3

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Observation ac8c3b1a-4898-4553-b7fe-2339183dc41b · inbound

Stabilizing black-box algorithms through task-oriented randomization cites this paper.

Stabilizing black-box algorithms through task-oriented randomization Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 3

Resolution: verified exact; arxiv_id, observed 2026-07-04T20:00:09.075955Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 0c8da1d8-e622-4afe-9454-5a2ff35bf923 · inbound

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? cites this paper.

When Does Synthetic Data Augmentation Improve Score-Based Imbalanced Classification? Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 78

Resolution: verified exact; arxiv_id, observed 2026-07-04T21:10:09.216394Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 711acd18-2c88-41a4-a555-a8de2928ef1e · inbound

Asymptotic Preservation and Uniform Accuracy of Diffusion and Flow-Matching Samplers cites this paper.

Asymptotic Preservation and Uniform Accuracy of Diffusion and Flow-Matching Samplers Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 14

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Observation 92308cba-5f21-4765-986f-78341d968443 · inbound

Minimum Norm Interpolation via The Local Theory of Banach Spaces: The Role of Gaussianity cites this paper.

Minimum Norm Interpolation via The Local Theory of Banach Spaces: The Role of Gaussianity Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 208

Resolution: verified exact; local_arxiv, observed 2026-07-09T02:35:53.717784Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation 43490ceb-b275-4a85-a236-20c777bc32ee · inbound

Weak Poincar\'e Inequalities via Approximate Stochastic Localization: Application to Sampling the Sherrington-Kirkpatrick Model cites this paper.

Weak Poincar\'e Inequalities via Approximate Stochastic Localization: Application to Sampling the Sherrington-Kirkpatrick Model Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 208

Resolution: verified exact; local_arxiv, observed 2026-07-10T12:17:04.039877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.

Observation f9a6e66f-4058-4919-a143-59207b2f9093 · inbound

FlashDiff: Efficient Regional Execution and Scheduling for Diffusion Model Serving cites this paper.

FlashDiff: Efficient Regional Execution and Scheduling for Diffusion Model Serving Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 6

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Observation 360fbde7-c648-4245-9d8f-14bf8221c031 · inbound

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models cites this paper.

From Score Learning to Discretized Sampling: An End-to-End Generalization Analysis of Diffusion Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-01T00:09:12.236614Z

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Observation a7ed7710-512f-4641-8302-da2e5e1d1ac5 · inbound

Diffusion Bootstrap for High-Dimensional Linear Models cites this paper.

Diffusion Bootstrap for High-Dimensional Linear Models Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 24

Resolution: unresolved; no resolver link, observed 2026-07-31T18:14:56.774028Z

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Observation 69e76853-d79b-4679-849e-94191838abbc · inbound

Denoising growth complexity: Data geometry and certified schedules for diffusion sampling cites this paper.

Denoising growth complexity: Data geometry and certified schedules for diffusion sampling Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-01T00:25:43.765684Z

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Observation b0815330-f90f-4575-85c1-22442a96095a · inbound

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers cites this paper.

DiverseDiT++: Quantifying, Analyzing, and Promoting Representation Diversity in Diffusion Transformers Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 84

Resolution: unresolved; no resolver link, observed 2026-08-08T01:07:12.475484Z

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