mlsanityData to 14 Oct 2026

A Jump-Diffusion Framework for Irregular Time Series Generation

arXiv 2505.23215 ↗First posted 29 May 2025math.NA cs.LG cs.NAcited by 2

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Abstract (arXiv)

We propose a framework for generative modeling of continuous-time processes from irregularly and asynchronously recorded data. It is based on the matching of generators and accommodates discontinuous trajectories. Analytical formulas for diffusion and jump bridges yield a family of reference generators that a neural network is trained to match. The key ingredient is that, for our constructed jump bridge, a parametrization of the jump kernel densities by scaled Gaussians admits closed-form expressions for the Kullback-Leibler divergence, allowing simulation-free training.

Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 27 of its 47 references that are in the map. First posted on arXiv 29 May 2025.

Builds on

27 of 47 references.

References · 27

  1. 2017Attention Is All You Needcited by 60,361
  2. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  3. 2020Score-Based Generative Modeling through Stochastic Differential Equationscited by 9,122
  4. 2015Deep Unsupervised Learning using Nonequilibrium Thermodynamicscited by 7,058
  5. 2022Flow Matching for Generative Modelingcited by 5,702
  6. 2018Neural Ordinary Differential Equationscited by 3,779
  7. 2022Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flowcited by 3,700
  8. 2023Stochastic Interpolants: A Unifying Framework for Flows and Diffusionscited by 939
  9. 2022DiffDock: Diffusion Steps, Twists, and Turns for Molecular Dockingcited by 313
  10. 2021Neural SDEs as Infinite-Dimensional GANscited by 161
  11. 2019Neural Jump Stochastic Differential Equationscited by 150
  12. 2018Deep Hedgingcited by 108
15 more
  1. 2019Theoretical guarantees for sampling and inference in generative models with latent diffusionscited by 97
  2. 2024Generator Matching: Generative modeling with arbitrary Markov processescited by 86
  3. 2021Efficient and Accurate Gradients for Neural SDEscited by 71
  4. 2023Functional Flow Matchingcited by 67
  5. 2024Probabilistic Forecasting with Stochastic Interpolants and F"ollmer Processescited by 66
  6. 2023KL Convergence Guarantees for Score diffusion models under minimal data assumptionscited by 63
  7. 2022From Denoising Diffusions to Denoising Markov Modelscited by 58
  8. 2022Modeling Temporal Data as Continuous Functions with Stochastic Process Diffusioncited by 39
  9. 2023Multilevel Diffusion: Infinite Dimensional Score-Based Diffusion Models for Image Generationcited by 37
  10. 2023Trans-Dimensional Generative Modeling via Jump Diffusion Modelscited by 35
  11. 2020Neural Jump Ordinary Differential Equations: Consistent Continuous-Time Prediction and Filteringcited by 34
  12. 2025SDE Matching: Scalable and Simulation-Free Training of Latent Stochastic Differential Equationscited by 23
  13. 2025Flow Matching: Markov Kernels, Stochastic Processes and Transport Planscited by 17
  14. 2025Probabilistic Forecasting via Autoregressive Flow Matchingcited by 9
  15. 2024Schr"odinger Bridge Problem for Jump Diffusionscited by 2

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026Is Flow Matching Just Trajectory Replay for Sequential Data?cited by 3
  2. 2026Prior-Fitted Functional Flow: In-Context Generative Models for Pharmacokineticscited by 0