{"total":54,"rows":[{"id":"2408.02320","title":"A Sharp Convergence Theory for The Probability Flow ODEs of Diffusion Models","created":"2024-08-05","cited_by_count":65,"area":"diffusion"},{"id":"2409.02426","title":"Breaking the Curse of Dimensionality: Diffusion Models Efficiently Learn Low-Dimensional Distributions","created":"2024-09-04","cited_by_count":53,"area":"diffusion"},{"id":"2409.18959","title":"O(d/T) Convergence Theory for Diffusion Probabilistic Models under Minimal Assumptions","created":"2024-09-27","cited_by_count":50,"area":"diffusion"},{"id":"2410.09046","title":"Linear Convergence of Diffusion Models Under the Manifold Hypothesis","created":"2024-10-11","cited_by_count":45,"area":"diffusion"},{"id":"2410.18784","title":"Denoising diffusion probabilistic models are optimally adaptive to unknown low dimensionality","created":"2024-10-24","cited_by_count":44,"area":"diffusion"},{"id":"2501.12982","title":"Low-dimensional adaptation of diffusion models: Convergence in total variation","created":"2025-01-22","cited_by_count":39,"area":"diffusion"},{"id":"2410.04760","title":"Stochastic Runge-Kutta Methods: Provable Acceleration of Diffusion Models","created":"2024-10-07","cited_by_count":35,"area":"diffusion"},{"id":"2410.23285","title":"Provable Acceleration for Diffusion Models under Minimal Assumptions","created":"2024-10-30","cited_by_count":27,"area":"diffusion"},{"id":"2503.09583","title":"Minimax Optimality of the Probability Flow ODE for Diffusion Models","created":"2025-03-12","cited_by_count":23,"area":"diffusion"},{"id":"2504.05300","title":"Dimension-Free Convergence of Diffusion Models for Approximate Gaussian Mixtures","created":"2025-04-07","cited_by_count":23,"area":"diffusion"},{"id":"2410.13738","title":"Instance-dependent Convergence Theory for Diffusion Models","created":"2024-10-17","cited_by_count":21,"area":"diffusion"},{"id":"2510.02305","title":"Diffusion Models and the Manifold Hypothesis: Log-Domain Smoothing is Geometry Adaptive","created":"2025-10-02","cited_by_count":20,"area":"diffusion"},{"id":"2603.22248","title":"Confidence-Based Decoding is Provably Efficient for Diffusion Language Models","created":"2026-03-23","cited_by_count":18,"area":"diffusion"},{"id":"2506.24042","title":"Faster Diffusion Models via Higher-Order Approximation","created":"2025-06-30","cited_by_count":16,"area":"diffusion"},{"id":"2510.27562","title":"Optimal Convergence Analysis of DDPM for General Distributions","created":"2025-10-31","cited_by_count":16,"area":"diffusion"},{"id":"2602.01338","title":"High-accuracy sampling for diffusion models and log-concave distributions","created":"2026-02-01","cited_by_count":16,"area":"diffusion"},{"id":"2502.19499","title":"On the Interpolation Effect of Score Smoothing in Diffusion Models","created":"2025-02-26","cited_by_count":15,"area":"diffusion"},{"id":"2501.03937","title":"A solvable model of learning generative diffusion: theory and insights","created":"2025-01-07","cited_by_count":14,"area":"diffusion"},{"id":"2410.11275","title":"Shallow diffusion networks provably learn hidden low-dimensional structure","created":"2024-10-15","cited_by_count":12,"area":"diffusion"},{"id":"2602.20126","title":"Adaptation to Intrinsic Dependence in Diffusion Language Models","created":"2026-02-23","cited_by_count":10,"area":"diffusion"},{"id":"2506.09681","title":"Assessing the Quality of Denoising Diffusion Models in Wasserstein Distance: Noisy Score and Optimal Bounds","created":"2025-06-11","cited_by_count":9,"area":"diffusion"},{"id":"2506.13061","title":"Fast Convergence for High-Order ODE Solvers in Diffusion Probabilistic Models","created":"2025-06-16","cited_by_count":9,"area":"diffusion"},{"id":"2602.07022","title":"Condition Errors Refinement in Autoregressive Image Generation with Diffusion Loss","created":"2026-02-02","cited_by_count":9,"area":"diffusion"},{"id":"2602.09639","title":"Blind denoising diffusion models and the blessings of dimensionality","created":"2026-02-10","cited_by_count":9,"area":"diffusion"},{"id":"2505.01382","title":"Provable Efficiency of Guidance in Diffusion Models for General Data Distribution","created":"2025-05-02","cited_by_count":8,"area":"diffusion"},{"id":"2512.04985","title":"Towards a unified framework for guided diffusion models","created":"2025-12-04","cited_by_count":8,"area":"diffusion"},{"id":"2505.20789","title":"Integrating Intermediate Layer Optimization and Projected Gradient Descent for Solving Inverse Problems with Diffusion Models","created":"2025-05-27","cited_by_count":7,"area":"diffusion"},{"id":"2511.04844","title":"Sublinear iterations can suffice even for DDPMs","created":"2025-11-06","cited_by_count":7,"area":"diffusion"},{"id":"2601.10708","title":"High-accuracy and dimension-free sampling with diffusions","created":"2026-01-15","cited_by_count":7,"area":"diffusion"},{"id":"2412.07435","title":"Parallel Simulation for Log-concave Sampling and Score-based Diffusion Models","created":"2024-12-10","cited_by_count":6,"area":"diffusion"},{"id":"2408.16765","title":"A Score-Based Density Formula, with Applications in Diffusion Generative Models","created":"2024-08-29","cited_by_count":4,"area":"diffusion"},{"id":"2601.18681","title":"ART for Diffusion Sampling: A Reinforcement Learning Approach to Timestep Schedule","created":"2026-01-26","cited_by_count":4,"area":"diffusion"},{"id":"2608.23554","title":"Provably adaptive sampling with uniform and remasking discrete diffusion models","created":"2026-08-24","cited_by_count":4,"area":"diffusion"},{"id":"2504.10653","title":"On the Contractivity of Stochastic Interpolation Flow","created":"2025-04-14","cited_by_count":3,"area":"diffusion"},{"id":"2509.03898","title":"Diffusion Generative Models Meet Compressed Sensing, with Applications to Imaging and Finance","created":"2025-09-04","cited_by_count":3,"area":"diffusion"},{"id":"2601.02499","title":"Polynomial Convergence of Riemannian Diffusion Models","created":"2026-01-05","cited_by_count":3,"area":"diffusion"},{"id":"2601.15500","title":"Low-Dimensional Adaptation of Rectified Flow: A Diffusion and Stochastic Localization Perspective","created":"2026-01-21","cited_by_count":3,"area":"diffusion"},{"id":"2605.30153","title":"Diffusion Models Are Statistically Optimal for Learning Low-Dimensional Multi-Modal Distributions","created":"2026-05-28","cited_by_count":3,"area":"diffusion"},{"id":"2607.08041","title":"An exact information theory of generalization phase transitions in Bayesian diffusion models","created":"2026-07-09","cited_by_count":3,"area":"diffusion"},{"id":"2505.18344","title":"Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access","created":"2025-05-23","cited_by_count":2,"area":"diffusion"},{"id":"2510.08409","title":"Optimal Stopping in Latent Diffusion Models","created":"2025-10-09","cited_by_count":2,"area":"diffusion"},{"id":"2605.18069","title":"Wasserstein bounds for denoising diffusion probabilistic models via the F\\\"ollmer process","created":"2026-05-18","cited_by_count":2,"area":"diffusion"},{"id":"2607.26285","title":"Denoising growth complexity: Data geometry and certified schedules for diffusion sampling","created":"2026-07-28","cited_by_count":2,"area":"diffusion"},{"id":"2406.01320","title":"Structural and Convergence Analysis of Discrete-Time Denoising Diffusion Probabilistic Models","created":"2024-06-03","cited_by_count":1,"area":"diffusion"},{"id":"2410.14730","title":"On the Relation Between Linear Diffusion and Power Iteration","created":"2024-10-16","cited_by_count":1,"area":"diffusion"},{"id":"2605.13448","title":"On the Limits of Latent Reuse in Diffusion Models","created":"2026-05-13","cited_by_count":1,"area":"diffusion"},{"id":"2606.19894","title":"Score Approximation for Diffusion Models on Arbitrary Low-Dimensional Structures","created":"2026-06-18","cited_by_count":1,"area":"diffusion"},{"id":"2606.23627","title":"Diffusion Models Adapt to Low-Dimensional Structure Under Flexible Coefficient Choices","created":"2026-06-22","cited_by_count":1,"area":"diffusion"},{"id":"2604.10074","title":"Transformers Learn the Optimal DDPM Denoiser for Multi-Token GMMs","created":"2026-04-11","cited_by_count":0,"area":"diffusion"},{"id":"2605.18040","title":"Connections between the F\\\"ollmer process and the denoising diffusion probabilistic model","created":"2026-05-18","cited_by_count":0,"area":"diffusion"},{"id":"2605.20235","title":"Provably Learning Diffusion Models under the Manifold Hypothesis: Collapse and Refine","created":"2026-05-16","cited_by_count":0,"area":"diffusion"},{"id":"2607.02137","title":"ART for Diffusion Sampling: Continuous-Time Control and Actor-Critic Learning","created":"2026-07-02","cited_by_count":0,"area":"diffusion"},{"id":"2608.19067","title":"Diffusion Models for High-Dimensional Clustered Data: Intrinsic-Dimension Adaptivity via Bayesian Classification","created":"2026-08-19","cited_by_count":0,"area":"diffusion"},{"id":"2609.33008","title":"A statistical theory for blind denoising: minimax estimation of the noise level from a single sample","created":"2026-09-26","cited_by_count":0,"area":"diffusion"}],"next_cursor":null}