Diffusion Models for Adaptive Sequential Data Generation | Paper Citation Record

Observation 5f31b805-4857-406a-aa10-8ea5b345cd43 · outbound

This paper cites Time-causal VAE: Robust financial time series generator.SIAM Journal on Financial Mathematics, 17(1):245–279, 2026.

Diffusion Models for Adaptive Sequential Data Generation Time-causal VAE: Robust financial time series generator.SIAM Journal on Financial Mathematics, 17(1):245–279, 2026

Reference 1

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Observation c8d7e8dd-3a44-4bbc-b874-d7863cdbc0fd · outbound

This paper cites Robust time series generation via Schr¨ odinger Bridge: a comprehensive evaluation.

Diffusion Models for Adaptive Sequential Data Generation Robust time series generation via Schr¨ odinger Bridge: a comprehensive evaluation

Reference 2

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Observation 49f539c3-bcfe-4ddf-9c69-2edac5b6e4f1 · outbound

This paper cites Tail-GAN: Learning to simulate tail risk scenarios.Management Science, 72(4):2917–2936, 2025.

Diffusion Models for Adaptive Sequential Data Generation Tail-GAN: Learning to simulate tail risk scenarios.Management Science, 72(4):2917–2936, 2025

Reference 3

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Observation 244f0b9f-adac-4fa6-8284-0147b0ec2053 · outbound

This paper cites Continuous-time mean-variance portfolio selection: A stochastic LQ frame- work.Applied Mathematics & Optimization, 42(1):19–33, 2000.

Diffusion Models for Adaptive Sequential Data Generation Continuous-time mean-variance portfolio selection: A stochastic LQ frame- work.Applied Mathematics & Optimization, 42(1):19–33, 2000

Reference 4

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Observation 39bf3d56-b9aa-4e76-9632-7343501d9697 · outbound

This paper cites Dynamic portfolio execution.Management Science, 65(5):2015–2040, 2019.

Diffusion Models for Adaptive Sequential Data Generation Dynamic portfolio execution.Management Science, 65(5):2015–2040, 2019

Reference 5

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Observation dee72459-8101-41e6-a59a-420d1f42a724 · outbound

This paper cites q-learning in continuous time.Journal of Machine Learning Research, 24(161):1–61, 2023.

Diffusion Models for Adaptive Sequential Data Generation q-learning in continuous time.Journal of Machine Learning Research, 24(161):1–61, 2023

Reference 6

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Observation 61d43426-394b-428a-897e-72fa5dbaf81b · outbound

This paper cites Data-driven generative simulation of SDEs using diffusion models.

Diffusion Models for Adaptive Sequential Data Generation Data-driven generative simulation of SDEs using diffusion models

Reference 7

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Observation fd45120f-44f8-48f7-b2c8-7e099a7f19fd · outbound

This paper cites Towards realistic market simulations: a generative adversarial networks approach.

Diffusion Models for Adaptive Sequential Data Generation Towards realistic market simulations: a generative adversarial networks approach

Reference 8

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Observation ef07c91b-ed3d-43db-a802-0860ed040c6a · outbound

This paper cites Mean--variance portfolio selection by continuous-time reinforcement learning: Algorithms, regret analysis, and empirical study.

Diffusion Models for Adaptive Sequential Data Generation Mean--variance portfolio selection by continuous-time reinforcement learning: Algorithms, regret analysis, and empirical study

Reference 9

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:56.042876Z

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Observation 638d2542-8469-4d2d-b3d8-6010bf457bcc · outbound

This paper cites Attention is all you need.Advances in neural information processing systems, 30, 2017.

Diffusion Models for Adaptive Sequential Data Generation Attention is all you need.Advances in neural information processing systems, 30, 2017

Reference 10

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Observation 506428c3-92bb-4d01-ac73-52391417a865 · outbound

This paper cites Taming transformers for high-resolution image synthesis.

Diffusion Models for Adaptive Sequential Data Generation Taming transformers for high-resolution image synthesis

Reference 11

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Observation a2f24019-8a3b-4b67-9e0e-844e61a3666e · outbound

This paper cites Offline reinforcement learning as one big sequence modeling problem.Advances in neural information processing systems, 34:1273–1286, 2021.

Diffusion Models for Adaptive Sequential Data Generation Offline reinforcement learning as one big sequence modeling problem.Advances in neural information processing systems, 34:1273–1286, 2021

Reference 12

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Observation 35a56743-11da-4f7a-88bf-78039d2af5e9 · outbound

This paper cites AdaCat: Adaptive Categorical Discretization for Autore- gressive Models.

Diffusion Models for Adaptive Sequential Data Generation AdaCat: Adaptive Categorical Discretization for Autore- gressive Models

Reference 13

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Observation 5ba476cb-62b9-4923-96d2-0adee4c54a98 · outbound

This paper cites Autoregressive image generation using residual quantization.

Diffusion Models for Adaptive Sequential Data Generation Autoregressive image generation using residual quantization

Reference 14

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Observation 2ad40642-49af-419f-8265-f397fac3dd66 · outbound

This paper cites Autoregressive image generation without vector quantization.

Diffusion Models for Adaptive Sequential Data Generation Autoregressive image generation without vector quantization

Reference 15

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Observation 18cf3f6b-d8a3-46b5-8b42-b405003a7b6e · outbound

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

Diffusion Models for Adaptive Sequential Data Generation Generative modeling by estimating gradients of the data distribution

Reference 16

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Observation 89bccb3c-15f7-4ec7-b6de-f5e99dd4c2a7 · outbound

This paper cites Score-based generative modeling through stochastic differential equations.

Diffusion Models for Adaptive Sequential Data Generation Score-based generative modeling through stochastic differential equations

Reference 17

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Observation c474588b-155f-4f39-93a4-c0f07dc66bc1 · outbound

This paper cites Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020.

Diffusion Models for Adaptive Sequential Data Generation Denoising diffusion probabilistic models.Advances in Neural Information Processing Systems, 33:6840–6851, 2020

Reference 18

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Observation 88ebd24c-ca49-4017-9b61-1ff66cbe841b · outbound

This paper cites Diffusion models beat GANs on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021.

Diffusion Models for Adaptive Sequential Data Generation Diffusion models beat GANs on image synthesis.Advances in neural information processing systems, 34:8780–8794, 2021

Reference 19

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Observation a42b5bbc-f2b2-46cf-9e0a-3ffd2909d2be · outbound

This paper cites High- resolution image synthesis with latent diffusion models.

Diffusion Models for Adaptive Sequential Data Generation High- resolution image synthesis with latent diffusion models

Reference 20

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Observation f78ba849-c9a7-4f2f-be7e-5eb9f0a26203 · outbound

This paper cites Improving image generation with better captions.OpenAI technical report, 2023.

Diffusion Models for Adaptive Sequential Data Generation Improving image generation with better captions.OpenAI technical report, 2023

Reference 21

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Observation 28cd8324-4842-4856-a95d-995ed41226b5 · outbound

This paper cites De novo design of protein structure and function with rfdiffusion.Nature, 620(7976):1089–1100, 2023.

Diffusion Models for Adaptive Sequential Data Generation De novo design of protein structure and function with rfdiffusion.Nature, 620(7976):1089–1100, 2023

Reference 22

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Observation b0a54300-15b6-4825-9349-fe3fa1a1f2dd · outbound

This paper cites Equivariant diffusion for molecule generation in 3D.

Diffusion Models for Adaptive Sequential Data Generation Equivariant diffusion for molecule generation in 3D

Reference 23

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Observation 0e0dc9bb-c02d-49ad-a512-b7b0088c3603 · outbound

This paper cites Conditional Generative Adversarial Nets.

Diffusion Models for Adaptive Sequential Data Generation Conditional Generative Adversarial Nets

Reference 24

Resolution: verified exact; local_arxiv, observed 2026-07-02T11:46:56.019180Z

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Observation a7ec081f-a6c6-4a58-95a4-71c7af9bff57 · outbound

This paper cites Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs.

Diffusion Models for Adaptive Sequential Data Generation Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs

Reference 25

Resolution: verified exact; local_arxiv, observed 2026-07-02T11:46:56.024711Z

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Observation 93085c06-92ff-4ef0-9a6c-928085606ac3 · outbound

This paper cites Time series simulation by condi- tional generative adversarial net.International Journal of Neural Networks and Advanced Applications, 7:25–38, 2020.

Diffusion Models for Adaptive Sequential Data Generation Time series simulation by condi- tional generative adversarial net.International Journal of Neural Networks and Advanced Applications, 7:25–38, 2020

Reference 26

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Observation 38a886fe-6ee6-4488-a450-ea3f0ac142aa · outbound

This paper cites Time-series generative adversarial networks.

Diffusion Models for Adaptive Sequential Data Generation Time-series generative adversarial networks

Reference 27

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Observation 11fac049-bea9-4f2d-8fb9-2a3449054bf6 · outbound

This paper cites Generative adversarial networks for financial trading strategies fine-tuning and combination.Quantitative Finance, 21(5):797–813, 2021.

Diffusion Models for Adaptive Sequential Data Generation Generative adversarial networks for financial trading strategies fine-tuning and combination.Quantitative Finance, 21(5):797–813, 2021

Reference 28

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Observation 7b5d697e-1e84-4ac5-8525-fbd3312aeb74 · outbound

This paper cites Sig- Wasserstein GANs for conditional time series generation.Mathematical Finance, 34(2):622–670, 2024.

Diffusion Models for Adaptive Sequential Data Generation Sig- Wasserstein GANs for conditional time series generation.Mathematical Finance, 34(2):622–670, 2024

Reference 29

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Observation a82ca43a-29af-4668-8e91-215ff3d3a851 · outbound

This paper cites Generating realistic stock market order streams.AAAI Conference on Artificial Intelligence, 34(01):727–734, 2020.

Diffusion Models for Adaptive Sequential Data Generation Generating realistic stock market order streams.AAAI Conference on Artificial Intelligence, 34(01):727–734, 2020

Reference 30

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Observation 8f2d5401-048c-4cb7-9124-d540562a67a7 · outbound

This paper cites Fin-GAN: Forecasting and classifying financial time series via generative adversarial networks.Quantitative Finance, 24(2):175–199, 2024.

Diffusion Models for Adaptive Sequential Data Generation Fin-GAN: Forecasting and classifying financial time series via generative adversarial networks.Quantitative Finance, 24(2):175–199, 2024

Reference 31

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Observation d5acd184-536b-438e-ae8e-9ff002468b3c · outbound

This paper cites VolGAN: A generative model for arbitrage-free implied volatility surfaces.Applied Mathematical Finance, 31(4):203–238, 2025.

Diffusion Models for Adaptive Sequential Data Generation VolGAN: A generative model for arbitrage-free implied volatility surfaces.Applied Mathematical Finance, 31(4):203–238, 2025

Reference 32

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Observation 7b5e89d7-caff-44f5-aa32-e81e354a2252 · outbound

This paper cites Quant GANs: Deep generation of financial time series.Quantitative Finance, 20(9):1419–1440, 2020.

Diffusion Models for Adaptive Sequential Data Generation Quant GANs: Deep generation of financial time series.Quantitative Finance, 20(9):1419–1440, 2020

Reference 33

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Observation 89738162-0782-41da-9e6f-922fd902131d · outbound

This paper cites PCF-GAN: generating sequential data via the characteristic function of measures on the path space.

Diffusion Models for Adaptive Sequential Data Generation PCF-GAN: generating sequential data via the characteristic function of measures on the path space

Reference 34

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Observation 3f345863-442a-4ad1-a1ca-da0abe70055a · outbound

This paper cites Generating financial markets with signatures.Available at SSRN 3657366, 2020.

Diffusion Models for Adaptive Sequential Data Generation Generating financial markets with signatures.Available at SSRN 3657366, 2020

Reference 35

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Observation e061a7f4-8734-4295-857a-0810634b01e0 · outbound

This paper cites A data-driven market simulator for small data environments.

Diffusion Models for Adaptive Sequential Data Generation A data-driven market simulator for small data environments

Reference 36

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Observation 061173a9-557e-4e0f-a729-5d3a9864af2a · outbound

This paper cites TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation.

Diffusion Models for Adaptive Sequential Data Generation TimeVAE: A Variational Auto-Encoder for Multivariate Time Series Generation

Reference 37

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:56.016903Z

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Observation a4e07d9f-dc31-474e-bf61-a535f3c1affd · outbound

This paper cites Multi-Asset Spot and Option Market Simulation.

Diffusion Models for Adaptive Sequential Data Generation Multi-Asset Spot and Option Market Simulation

Reference 38

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:56.038073Z

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Observation baebcff9-e177-45f2-81b7-36bb5d519b4f · outbound

This paper cites Hybrid variational autoencoder for time series forecasting.Knowledge-Based Systems, 281:111079, 2023.

Diffusion Models for Adaptive Sequential Data Generation Hybrid variational autoencoder for time series forecasting.Knowledge-Based Systems, 281:111079, 2023

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Observation 0e636278-2c18-4bfc-81c0-8887ef20b1ea · outbound

This paper cites Time-transformer: Integrating local and global features for better time series generation.

Diffusion Models for Adaptive Sequential Data Generation Time-transformer: Integrating local and global features for better time series generation

Reference 40

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Observation e0491743-4dfd-44ee-8752-4da7649316d2 · outbound

This paper cites Generative learning for financial time series with irreg- ular and scale-invariant patterns.

Diffusion Models for Adaptive Sequential Data Generation Generative learning for financial time series with irreg- ular and scale-invariant patterns

Reference 41

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Observation 0bc67641-87c9-44f9-b154-b89946cc021f · outbound

This paper cites Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting.

Diffusion Models for Adaptive Sequential Data Generation Autoregressive denoising diffusion models for multivariate probabilistic time series forecasting

Reference 42

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Observation f93a2fe2-77cf-4994-b0b8-ea8ae2041a34 · outbound

This paper cites Regular Time-series Generation using SGM.

Diffusion Models for Adaptive Sequential Data Generation Regular Time-series Generation using SGM

Reference 43

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:56.021921Z

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Observation 8d38450d-e8ca-44f2-bfba-c34d89d8f3ac · outbound

This paper cites TSGM: Regular and irregular time-series generation using score-based generative models.arXiv preprint arXiv:2511.21335, 2025.

Diffusion Models for Adaptive Sequential Data Generation TSGM: Regular and irregular time-series generation using score-based generative models.arXiv preprint arXiv:2511.21335, 2025

Reference 44

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:56.031261Z

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Observation 0e6a3927-d480-4625-87ac-8b7c19dbad0f · outbound

This paper cites Diffusion-TS: Interpretable diffusion for general time series generation.

Diffusion Models for Adaptive Sequential Data Generation Diffusion-TS: Interpretable diffusion for general time series generation

Reference 45

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Observation b9178fa8-8ea2-4c39-ab8e-3378e4adc7f3 · outbound

This paper cites Utilizing image transforms and diffusion models for generative modeling of short and long time series.

Diffusion Models for Adaptive Sequential Data Generation Utilizing image transforms and diffusion models for generative modeling of short and long time series

Reference 46

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Observation e8e56e6f-79d2-4514-90c3-d75b591eb8bc · outbound

This paper cites A survey on diffusion models for time series and spatio-temporal data.ACM Computing Surveys, 58(8):1–39, 2026.

Diffusion Models for Adaptive Sequential Data Generation A survey on diffusion models for time series and spatio-temporal data.ACM Computing Surveys, 58(8):1–39, 2026

Reference 47

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Observation 87554314-2e1c-434e-9094-8224e7bc1a7d · outbound

This paper cites Dynamic data generation and dynamic portfolio selection: an application of a score-based diffusion model.

Diffusion Models for Adaptive Sequential Data Generation Dynamic data generation and dynamic portfolio selection: an application of a score-based diffusion model

Reference 48

Resolution: metadata mismatch; arxiv_id, observed 2026-08-04T00:35:57.786870Z

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Observation 71bde4db-59ce-4564-aae9-740254a77aac · outbound

This paper cites Deep generative learning via Schr¨ odinger bridge.

Diffusion Models for Adaptive Sequential Data Generation Deep generative learning via Schr¨ odinger bridge

Reference 49

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Observation 60d342c9-4997-4d62-ab42-90a9390e5e08 · outbound

This paper cites Diffusion Schr¨ odinger bridge with applications to score-based generative modeling.

Diffusion Models for Adaptive Sequential Data Generation Diffusion Schr¨ odinger bridge with applications to score-based generative modeling

Reference 50

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Observation 8e7ed64a-58f8-41c5-a4cc-1d2ed4b4e337 · outbound

This paper cites Generative modeling for time series via Schr{\"o}dinger bridge.

Diffusion Models for Adaptive Sequential Data Generation Generative modeling for time series via Schr{\"o}dinger bridge

Reference 51

Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:46:56.034964Z

Source-reported events for the cited work

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Observation cb9e7749-c8e5-45f4-923d-da71bd41c8aa · outbound

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

Diffusion Models for Adaptive Sequential Data Generation Sampling is as easy as learning the score: theory for diffusion models with minimal data assumptions

Reference 52

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Observation 95dcb649-abfa-4bce-8a57-42a55c29409f · outbound

This paper cites Convergence for score-based generative modeling with poly- nomial complexity.Advances in Neural Information Processing Systems, 35:22870–22882, 2022.

Diffusion Models for Adaptive Sequential Data Generation Convergence for score-based generative modeling with poly- nomial complexity.Advances in Neural Information Processing Systems, 35:22870–22882, 2022

Reference 53

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Observation e1c42daf-650e-4692-a057-dd6de5350b59 · outbound

This paper cites Let us build bridges: Understanding and extending diffusion generative models.

Diffusion Models for Adaptive Sequential Data Generation Let us build bridges: Understanding and extending diffusion generative models

Reference 54

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Observation c3122e27-abe8-42e9-ad68-2a7163db5711 · outbound

This paper cites Convergence of score-based generative modeling for general data distributions.

Diffusion Models for Adaptive Sequential Data Generation Convergence of score-based generative modeling for general data distributions

Reference 55

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Observation 80ef5efc-f275-41ea-a4bc-fbcd80fffd1a · outbound

This paper cites Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions.

Diffusion Models for Adaptive Sequential Data Generation Improved analysis of score-based generative modeling: User-friendly bounds under minimal smoothness assumptions

Reference 56

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Observation 2e14b5d6-d986-4d29-8cbc-e3b8b5fc21c6 · outbound

This paper cites Nearlyd-linear convergence bounds for diffusion models via stochastic localization.

Diffusion Models for Adaptive Sequential Data Generation Nearlyd-linear convergence bounds for diffusion models via stochastic localization

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Observation 5eab4274-5fd7-4005-a20a-e1a462c0c5af · outbound

This paper cites Restoration-degradation beyond linear diffu- sions: A non-asymptotic analysis for DDIM-type samplers.

Diffusion Models for Adaptive Sequential Data Generation Restoration-degradation beyond linear diffu- sions: A non-asymptotic analysis for DDIM-type samplers

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Observation 59622253-8332-42f2-b543-426d27152c56 · outbound

This paper cites Towards non-asymptotic convergence for diffusion- based generative models.

Diffusion Models for Adaptive Sequential Data Generation Towards non-asymptotic convergence for diffusion- based generative models

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Observation 17b04eb4-2eb6-4faf-ad22-196bc46cb3da · outbound

This paper cites Improved convergence of score-based diffusion models via prediction-correction.Transactions on Machine Learning Research, 2024.

Diffusion Models for Adaptive Sequential Data Generation Improved convergence of score-based diffusion models via prediction-correction.Transactions on Machine Learning Research, 2024

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Observation 86ba5861-4844-497f-a43c-9b6ac3b0ff1e · outbound

This paper cites Convergence of flow-based generative models via proximal gradient descent in Wasserstein space.IEEE Transactions on Information Theory, 70(11): 8087–8106, 2024.

Diffusion Models for Adaptive Sequential Data Generation Convergence of flow-based generative models via proximal gradient descent in Wasserstein space.IEEE Transactions on Information Theory, 70(11): 8087–8106, 2024

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Observation 8ffc0cb9-8daf-4926-a211-731bb15c18f1 · outbound

This paper cites Convergence analysis of probability flow ODE for score-based generative models.IEEE Transactions on Information Theory, 71(6):4581–4601, 2025.

Diffusion Models for Adaptive Sequential Data Generation Convergence analysis of probability flow ODE for score-based generative models.IEEE Transactions on Information Theory, 71(6):4581–4601, 2025

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Observation 722e28e8-1437-425b-a342-1d1b07a08e0f · outbound

This paper cites Broadening target distributions for accel- erated diffusion models via a novel analysis approach.

Diffusion Models for Adaptive Sequential Data Generation Broadening target distributions for accel- erated diffusion models via a novel analysis approach

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Observation 80c0076c-eeea-4502-a309-49e246b2554f · outbound

This paper cites an unresolved cited work.

Diffusion Models for Adaptive Sequential Data Generation Unresolved cited work

Reference 64

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Observation 3ee3a18f-3e64-432e-9d8c-2ee56785c0d3 · outbound

This paper cites Contractive Diffusion Probabilistic Models.

Diffusion Models for Adaptive Sequential Data Generation Contractive Diffusion Probabilistic Models

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Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:56.011160Z

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Observation 9c1f66a9-d6ab-4a7f-8c5b-8a988f4a713c · outbound

This paper cites Unified convergence analysis for score-based diffusion models with deterministic samplers.

Diffusion Models for Adaptive Sequential Data Generation Unified convergence analysis for score-based diffusion models with deterministic samplers

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Observation 1a53a613-e916-4558-a4cd-6ca230e77a84 · outbound

This paper cites Rotskoff, and Lexing Ying.

Diffusion Models for Adaptive Sequential Data Generation Rotskoff, and Lexing Ying

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Observation 6d2ace49-c660-4db6-b74a-97f3333e3137 · outbound

This paper cites Convergence analysis for general probability flow ODEs of diffusion models in Wasserstein distances.

Diffusion Models for Adaptive Sequential Data Generation Convergence analysis for general probability flow ODEs of diffusion models in Wasserstein distances

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Observation de1d08b2-f40d-464d-a47f-eac78aa95f89 · outbound

This paper cites Beyond log-concavity and score regularity: Improved convergence bounds for score-based generative models in W2-distance.

Diffusion Models for Adaptive Sequential Data Generation Beyond log-concavity and score regularity: Improved convergence bounds for score-based generative models in W2-distance

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Observation 45520729-818c-4f39-9a5b-9e33b2a0efa4 · outbound

This paper cites On sta- tistical rates of conditional diffusion transformers: Approximation, estimation and minimax optimality.

Diffusion Models for Adaptive Sequential Data Generation On sta- tistical rates of conditional diffusion transformers: Approximation, estimation and minimax optimality

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Observation 9f986f02-3dd2-4949-95e9-320c0f8a1c87 · outbound

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

Diffusion Models for Adaptive Sequential Data Generation Low-dimensional adaptation of diffusion models: Convergence in total variation

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Observation ba453570-88f5-4b5c-8c39-0756cf751adc · outbound

This paper cites Gottwald, Shuigen Liu, Youssef Marzouk, Sebas tian Reich, and Xin T.

Diffusion Models for Adaptive Sequential Data Generation Gottwald, Shuigen Liu, Youssef Marzouk, Sebas tian Reich, and Xin T

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Resolution: metadata mismatch; arxiv_id, observed 2026-07-02T11:46:56.004818Z

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Observation a91030e9-1f1a-4d76-aca9-4f6a3a130ecf · outbound

This paper cites an unresolved cited work.

Diffusion Models for Adaptive Sequential Data Generation Unresolved cited work

Reference 73

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Observation c14c26df-92f3-474e-a0ac-edb6b31aec64 · outbound

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

Diffusion Models for Adaptive Sequential Data Generation Diffusion Model for Manifold Data: Score Decomposition, Curvature, and Statistical Complexity

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Resolution: verified exact; local_arxiv, observed 2026-07-02T11:46:56.001162Z

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Observation 6e99f658-ddf9-4cba-a437-5517f763a172 · outbound

This paper cites Diffusion models are minimax optimal distribution estimators.

Diffusion Models for Adaptive Sequential Data Generation Diffusion models are minimax optimal distribution estimators

Reference 75

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Observation 43a32991-9219-4463-b879-431747cd3438 · outbound

This paper cites Optimal score estimation via empirical Bayes smoothing.

Diffusion Models for Adaptive Sequential Data Generation Optimal score estimation via empirical Bayes smoothing

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Observation df8c9cbe-e6b3-4b25-94a9-41d99d3d9044 · outbound

This paper cites Minimax optimality of score-based diffusion models: Beyond the density lower bound assumptions.

Diffusion Models for Adaptive Sequential Data Generation Minimax optimality of score-based diffusion models: Beyond the density lower bound assumptions

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Observation c5d35bda-397f-46c0-853f-116b9bece31f · outbound

This paper cites Neural network-based score estimation in diffu- sion models: Optimization and generalization.

Diffusion Models for Adaptive Sequential Data Generation Neural network-based score estimation in diffu- sion models: Optimization and generalization

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Observation af3d5a84-f89b-407a-a306-ee880290d481 · outbound

This paper cites From optimal score matching to optimal sampling.

Diffusion Models for Adaptive Sequential Data Generation From optimal score matching to optimal sampling

Reference 79

Resolution: verified exact; arxiv_id, observed 2026-07-02T11:46:56.014152Z

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Observation 5566ada0-6e6c-42b4-893f-94ba773e89ad · outbound

This paper cites Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data.

Diffusion Models for Adaptive Sequential Data Generation Score approximation, estimation and distribution recovery of diffusion models on low-dimensional data

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Observation 069f9f9c-279e-4512-b359-db76b7e63d25 · outbound

This paper cites Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory.

Diffusion Models for Adaptive Sequential Data Generation Unveil Conditional Diffusion Models with Classifier-free Guidance: A Sharp Statistical Theory

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Observation 64bc0626-c15a-42a7-aae7-934822ce6982 · outbound

This paper cites Diffusion transformer captures spatial-temporal dependencies: A theory for gaussian process data.

Diffusion Models for Adaptive Sequential Data Generation Diffusion transformer captures spatial-temporal dependencies: A theory for gaussian process data

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Observation bc4c7ef8-63ef-4cab-b29e-3f97ad0ef6d1 · outbound

This paper cites Reverse-time diffusion equation models.Stochastic Processes and their Applica- tions, 12(3):313–326, 1982.

Diffusion Models for Adaptive Sequential Data Generation Reverse-time diffusion equation models.Stochastic Processes and their Applica- tions, 12(3):313–326, 1982

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Observation 37e88702-302c-42e8-b389-63dc2878f5fc · outbound

This paper cites Time reversal of diffusions.The Annals of Probability, pages 1188–1205, 1986.

Diffusion Models for Adaptive Sequential Data Generation Time reversal of diffusions.The Annals of Probability, pages 1188–1205, 1986

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Observation a4e942da-84b5-4c0d-9431-09448b067935 · outbound

This paper cites Improved denoising diffusion probabilistic models.

Diffusion Models for Adaptive Sequential Data Generation Improved denoising diffusion probabilistic models

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Observation e9daeb66-6b48-4787-acfb-9c40702b4628 · outbound

This paper cites A connection between score matching and denoising autoencoders.Neural computation, 23(7):1661–1674, 2011.

Diffusion Models for Adaptive Sequential Data Generation A connection between score matching and denoising autoencoders.Neural computation, 23(7):1661–1674, 2011

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Observation d524996d-4ef5-4f7d-85ff-f33b6535ce7d · outbound

This paper cites Long-term memory in stock market prices.Econometrica: Journal of the Econometric Society, pages 1279–1313, 1991.

Diffusion Models for Adaptive Sequential Data Generation Long-term memory in stock market prices.Econometrica: Journal of the Econometric Society, pages 1279–1313, 1991

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Observation b29aced8-e738-4b96-9c39-653a2690ee72 · outbound

This paper cites Empirical properties of asset returns: stylized facts and statistical issues.Quantitative finance, 1(2):223, 2001.

Diffusion Models for Adaptive Sequential Data Generation Empirical properties of asset returns: stylized facts and statistical issues.Quantitative finance, 1(2):223, 2001

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Observation 3bee3255-9f88-4573-acf7-e12a419deccd · outbound

This paper cites Conditional diffusion models are minimax-optimal and manifold- adaptive for conditional distribution estimation.

Diffusion Models for Adaptive Sequential Data Generation Conditional diffusion models are minimax-optimal and manifold- adaptive for conditional distribution estimation

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Observation 5113788d-a12b-43b7-8d20-f2370ea04501 · outbound

This paper cites On the autocorrelation of the stock market.Journal of Financial Econometrics, 19(1): 39–52, 2021.

Diffusion Models for Adaptive Sequential Data Generation On the autocorrelation of the stock market.Journal of Financial Econometrics, 19(1): 39–52, 2021

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Observation e8162991-e877-40e8-9a7f-7a2ca702c715 · outbound

This paper cites The variation of certain speculative prices.Journal of business, 36(4):394, 1963.

Diffusion Models for Adaptive Sequential Data Generation The variation of certain speculative prices.Journal of business, 36(4):394, 1963

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Observation 87e18051-2520-4d20-888c-a70a96c742ad · outbound

This paper cites Statistical prop- erties of share volume traded in financial markets.Physical review e, 62(4):R4493, 2000.

Diffusion Models for Adaptive Sequential Data Generation Statistical prop- erties of share volume traded in financial markets.Physical review e, 62(4):R4493, 2000

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Observation 8d792ae3-4344-486d-a8de-e2a36188eade · outbound

This paper cites Breaking the curse of dimensionality with convex neural networks.The Journal of Machine Learning Research, 18(1):629–681, 2017.

Diffusion Models for Adaptive Sequential Data Generation Breaking the curse of dimensionality with convex neural networks.The Journal of Machine Learning Research, 18(1):629–681, 2017

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Observation 024dd843-cbbe-4675-9918-9361f16f9ee8 · outbound

This paper cites Are transformers with one layer self-attention using low-rank weight ma- trices universal approximators? InThe Twelfth International Conference on Learning Representations, 2024.

Diffusion Models for Adaptive Sequential Data Generation Are transformers with one layer self-attention using low-rank weight ma- trices universal approximators? InThe Twelfth International Conference on Learning Representations, 2024

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Observation 4779655f-c0c9-4c1a-864f-47a334cca00d · outbound

This paper cites Continuous-time mean–variance portfolio selection: A reinforcement learning framework.Mathematical Finance, 30(4):1273–1308, 2020.

Diffusion Models for Adaptive Sequential Data Generation Continuous-time mean–variance portfolio selection: A reinforcement learning framework.Mathematical Finance, 30(4):1273–1308, 2020

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Observation 38ddc150-496b-48df-a1e9-6f114145a5ee · outbound

This paper cites It follows that X h 0 |X [k:h−1] 0 =z∼ N µh +Σ 21Σ−1 11 (z−µ [k:h−1]),Σ 22 −Σ 21Σ−1 11 Σ12.

Diffusion Models for Adaptive Sequential Data Generation It follows that X h 0 |X [k:h−1] 0 =z∼ N µh +Σ 21Σ−1 11 (z−µ [k:h−1]),Σ 22 −Σ 21Σ−1 11 Σ12

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