{"total":84,"rows":[{"id":"2303.08797","title":"Stochastic Interpolants: A Unifying Framework for Flows and Diffusions","created":"2023-03-15","cited_by_count":939,"area":null},{"id":"2305.19256","title":"Ambient Diffusion: Learning Clean Distributions from Corrupted Data","created":"2023-05-30","cited_by_count":111,"area":null},{"id":"2210.00726","title":"Statistical Efficiency of Score Matching: The View from Isoperimetry","created":"2022-10-03","cited_by_count":64,"area":null},{"id":"2410.13782","title":"DPLM-2: A Multimodal Diffusion Protein Language Model","created":"2024-10-17","cited_by_count":64,"area":null},{"id":"2402.07747","title":"Optimal score estimation via empirical Bayes smoothing","created":"2024-02-12","cited_by_count":61,"area":null},{"id":"2310.02679","title":"Diffusion Generative Flow Samplers: Improving learning signals through partial trajectory optimization","created":"2023-10-04","cited_by_count":57,"area":null},{"id":"2302.10249","title":"Faster high-accuracy log-concave sampling via algorithmic warm starts","created":"2023-02-20","cited_by_count":45,"area":null},{"id":"2307.02037","title":"Reverse Diffusion Monte Carlo","created":"2023-07-05","cited_by_count":41,"area":null},{"id":"2310.03575","title":"Analysis of learning a flow-based generative model from limited sample complexity","created":"2023-10-05","cited_by_count":38,"area":null},{"id":"2512.01809","title":"Much Ado About Noising: Dispelling the Myths of Generative Robotic Control","created":"2025-12-01","cited_by_count":36,"area":null},{"id":"2310.17582","title":"Convergence of flow-based generative models via proximal gradient descent in Wasserstein space","created":"2023-10-26","cited_by_count":34,"area":null},{"id":"2307.14619","title":"Provable Guarantees for Generative Behavior Cloning: Bridging Low-Level Stability and High-Level Behavior","created":"2023-07-27","cited_by_count":33,"area":null},{"id":"2310.09031","title":"MINDE: Mutual Information Neural Diffusion Estimation","created":"2023-10-13","cited_by_count":31,"area":null},{"id":"2503.01006","title":"Underdamped Diffusion Bridges with Applications to Sampling","created":"2025-03-02","cited_by_count":29,"area":null},{"id":"2402.10758","title":"Stochastic Localization via Iterative Posterior Sampling","created":"2024-02-16","cited_by_count":28,"area":null},{"id":"2305.17476","title":"Toward Understanding Generative Data Augmentation","created":"2023-05-27","cited_by_count":25,"area":null},{"id":"2310.15290","title":"Reliable Generation of Privacy-preserving Synthetic Electronic Health Record Time Series via Diffusion Models","created":"2023-10-23","cited_by_count":24,"area":null},{"id":"2407.16936","title":"Provable Benefit of Annealed Langevin Monte Carlo for Non-log-concave Sampling","created":"2024-07-24","cited_by_count":23,"area":null},{"id":"2410.10516","title":"UniGEM: A Unified Approach to Generation and Property Prediction for Molecules","created":"2024-10-14","cited_by_count":21,"area":null},{"id":"2401.07187","title":"A Survey on Statistical Theory of Deep Learning: Approximation, Training Dynamics, and Generative Models","created":"2024-01-14","cited_by_count":20,"area":null},{"id":"2405.06780","title":"Deep MMD Gradient Flow without adversarial training","created":"2024-05-10","cited_by_count":19,"area":null},{"id":"2406.01461","title":"Hardness of Learning Neural Networks under the Manifold Hypothesis","created":"2024-06-03","cited_by_count":18,"area":null},{"id":"2411.02780","title":"How much is a noisy image worth? Data Scaling Laws for Ambient Diffusion","created":"2024-11-05","cited_by_count":18,"area":null},{"id":"2311.11475","title":"Gaussian Interpolation Flows","created":"2023-11-20","cited_by_count":17,"area":null},{"id":"2310.18123","title":"Sample Complexity Bounds for Score-Matching: Causal Discovery and Generative Modeling","created":"2023-10-27","cited_by_count":16,"area":null},{"id":"2502.17100","title":"Generative Models in Decision Making: A Survey","created":"2025-02-24","cited_by_count":15,"area":null},{"id":"2410.03655","title":"Geometric Representation Condition Improves Equivariant Molecule Generation","created":"2024-10-04","cited_by_count":13,"area":null},{"id":"2502.05623","title":"Mixing Time of the Proximal Sampler in Relative Fisher Information via Strong Data Processing Inequality","created":"2025-02-08","cited_by_count":12,"area":null},{"id":"2305.01777","title":"Representation Learning via Manifold Flattening and Reconstruction","created":"2023-05-02","cited_by_count":11,"area":null},{"id":"2409.20547","title":"Annealing Flow Generative Models Towards Sampling High-Dimensional and Multi-Modal Distributions","created":"2024-09-30","cited_by_count":11,"area":null},{"id":"2410.10699","title":"Fast Convergence of $\\Phi$-Divergence Along the Unadjusted Langevin Algorithm and Proximal Sampler","created":"2024-10-14","cited_by_count":11,"area":null},{"id":"2502.04575","title":"Complexity Analysis of Normalizing Constant Estimation: from Jarzynski Equality to Annealed Importance Sampling and beyond","created":"2025-02-07","cited_by_count":11,"area":null},{"id":"2303.03237","title":"Convergence Rates for Non-Log-Concave Sampling and Log-Partition Estimation","created":"2023-03-06","cited_by_count":10,"area":null},{"id":"2310.01762","title":"Sampling Multimodal Distributions with the Vanilla Score: Benefits of Data-Based Initialization","created":"2023-10-03","cited_by_count":10,"area":null},{"id":"2310.20579","title":"Initialization Matters: Privacy-Utility Analysis of Overparameterized Neural Networks","created":"2023-10-31","cited_by_count":10,"area":null},{"id":"2312.05579","title":"Conditional Stochastic Interpolation for Generative Learning","created":"2023-12-09","cited_by_count":9,"area":null},{"id":"2608.13520","title":"The data geometry of masking diffusion: Certified-optimal schedules via unmasking growth complexity","created":"2026-08-13","cited_by_count":9,"area":null},{"id":"2408.11686","title":"Plug-in estimation of Schr\\\"odinger bridges","created":"2024-08-21","cited_by_count":8,"area":null},{"id":"2411.13443","title":"Nonlinear Assimilation via Score-based Sequential Langevin Sampling","created":"2024-11-20","cited_by_count":8,"area":null},{"id":"2405.16837","title":"Enhancing Accuracy in Generative Models via Knowledge Transfer","created":"2024-05-27","cited_by_count":7,"area":null},{"id":"2509.19707","title":"Diffusion and Flow-based Copulas: Forgetting and Remembering Dependencies","created":"2025-09-24","cited_by_count":7,"area":null},{"id":"2511.05924","title":"DiScoFormer: Plug-In Density and Score Estimation with Transformers","created":"2025-11-08","cited_by_count":6,"area":null},{"id":"2512.10857","title":"Generative Modeling from Black-box Corruptions via Self-Consistent Stochastic Interpolants","created":"2025-12-11","cited_by_count":6,"area":null},{"id":"2410.04811","title":"Learning Efficient and Effective Trajectories for Differential Equation-based Image Restoration","created":"2024-10-07","cited_by_count":5,"area":null},{"id":"2502.06200","title":"On the query complexity of sampling from non-log-concave distributions","created":"2025-02-10","cited_by_count":5,"area":null},{"id":"2509.02971","title":"Scale-Adaptive Generative Flows for Multiscale Scientific Data","created":"2025-09-03","cited_by_count":5,"area":null},{"id":"2405.10392","title":"Transport based particle methods for the Fokker-Planck-Landau equation","created":"2024-05-16","cited_by_count":4,"area":null},{"id":"2405.13962","title":"Robust Generative Learning with Lipschitz-Regularized $\\alpha$-Divergences Allows Minimal Assumptions on Target Distributions","created":"2024-05-22","cited_by_count":4,"area":null},{"id":"2407.19448","title":"Piecewise deterministic generative models","created":"2024-07-28","cited_by_count":4,"area":null},{"id":"2501.00565","title":"Sampling from multi-modal distributions with polynomial query complexity in fixed dimension via reverse diffusion","created":"2024-12-31","cited_by_count":4,"area":null},{"id":"2502.09130","title":"Finite-Time Analysis of Discrete-Time Stochastic Interpolants","created":"2025-02-13","cited_by_count":4,"area":null},{"id":"2504.18130","title":"Score-based deterministic density sampling","created":"2025-04-25","cited_by_count":4,"area":null},{"id":"2512.24152","title":"Fast Score-Based Sampling via Log-Concave Reductions","created":"2025-12-30","cited_by_count":4,"area":null},{"id":"2404.03272","title":"Cryptographic Hardness of Score Estimation","created":"2024-04-04","cited_by_count":3,"area":null},{"id":"2409.01688","title":"Differentially Private Kernel Density Estimation","created":"2024-09-03","cited_by_count":3,"area":null},{"id":"2409.17294","title":"Schr\\\"odinger bridge based deep conditional generative learning","created":"2024-09-25","cited_by_count":3,"area":null},{"id":"2508.14351","title":"A Non-Asymptotic Convergent Analysis for Scored-Based Graph Generative Model via a System of Stochastic Differential Equations","created":"2025-08-20","cited_by_count":3,"area":null},{"id":"2510.20871","title":"Exponential Convergence Guarantees for Iterative Markovian Fitting","created":"2025-10-23","cited_by_count":3,"area":null},{"id":"2511.01795","title":"Fractional Diffusion Bridge Models","created":"2025-11-03","cited_by_count":3,"area":null},{"id":"2512.23643","title":"Simultaneous Approximation of the Score Function and Its Derivatives by Deep Neural Networks","created":"2025-12-29","cited_by_count":3,"area":null},{"id":"2602.01449","title":"Dimension-Free Multimodal Sampling via Preconditioned Annealed Langevin Dynamics","created":"2026-02-01","cited_by_count":3,"area":null},{"id":"2605.13175","title":"Do Heavy Tails Help Diffusion? On the Subtle Trade-off Between Initialization and Training","created":"2026-05-13","cited_by_count":3,"area":null},{"id":"2306.00181","title":"Conditionally Strongly Log-Concave Generative Models","created":"2023-05-31","cited_by_count":2,"area":null},{"id":"2510.04460","title":"Perspectives on Stochastic Localization","created":"2025-10-06","cited_by_count":2,"area":null},{"id":"2512.10401","title":"Diffusion differentiable resampling","created":"2025-12-11","cited_by_count":2,"area":null},{"id":"2605.25567","title":"Rao-Blackwellized Score Matching on Manifolds","created":"2026-05-25","cited_by_count":2,"area":null},{"id":"2502.14583","title":"A Theory for Conditional Generative Modeling on Multiple Data Sources","created":"2025-02-20","cited_by_count":1,"area":null},{"id":"2508.15069","title":"Sampling by averaging: A multiscale approach to score estimation","created":"2025-08-20","cited_by_count":1,"area":null},{"id":"2602.10989","title":"Variational Optimality of F\\\"ollmer Processes in Generative Diffusions","created":"2026-02-11","cited_by_count":1,"area":null},{"id":"2602.19512","title":"Variational Trajectory Optimization of Anisotropic Diffusion Schedules","created":"2026-02-23","cited_by_count":1,"area":null},{"id":"2608.03916","title":"Trajectory inference via Acceleration Matching","created":"2026-08-04","cited_by_count":1,"area":null},{"id":"2502.03587","title":"Stein Discrepancy for Unsupervised Domain Adaptation","created":"2025-02-05","cited_by_count":0,"area":null},{"id":"2507.11236","title":"Improved sampling algorithms and functional inequalities for non-log-concave distributions","created":"2025-07-15","cited_by_count":0,"area":null},{"id":"2603.18817","title":"Seasoning Generative Models for a Generalization Aftertaste","created":"2026-03-19","cited_by_count":0,"area":null},{"id":"2605.06172","title":"Expressivity of Bi-Lipschitz Normalizing Flows: A Score-Based Diffusion Perspective","created":"2026-05-07","cited_by_count":0,"area":null},{"id":"2605.27769","title":"Smoothed Score Queries and the Complexity of Sampling","created":"2026-05-26","cited_by_count":0,"area":null},{"id":"2606.00934","title":"Efficient Synthetic Network Generation via Latent Embedding Reconstruction","created":"2026-05-31","cited_by_count":0,"area":null},{"id":"2606.09705","title":"When Do Local Score Models Extrapolate Across Size? A Diagnostic Theory and Benchmark","created":"2026-06-08","cited_by_count":0,"area":null},{"id":"2606.25269","title":"Stabilizing black-box algorithms through task-oriented randomization","created":"2026-06-24","cited_by_count":0,"area":null},{"id":"2607.16725","title":"Semi-Supervised Conditional Generative Learning through Stochastic Interpolation and Sufficient Representations","created":"2026-07-18","cited_by_count":0,"area":null},{"id":"2607.19004","title":"The Tractability Landscape of Sampling with Inexact Scores","created":"2026-07-21","cited_by_count":0,"area":null},{"id":"2607.26955","title":"Breaking the Curse with BAND: Nonparametric Distribution Estimation in High Dimensions","created":"2026-07-29","cited_by_count":0,"area":null},{"id":"2609.28338","title":"Local Geometric Mixing via Dobrushin Contraction with Applications to Diffusion Path Monte Carlo and the Proximal Sampler","created":"2026-09-23","cited_by_count":0,"area":null},{"id":"2610.15078","title":"On the Theoretical Limits of Learning Unconstrained Interatomic Potentials","created":"2026-10-10","cited_by_count":0,"area":null}],"next_cursor":null}