{"total":26,"rows":[{"id":"2106.10410","title":"Deep Generative Learning via Schr\\\"{o}dinger Bridge","created":"2021-06-19","cited_by_count":104,"area":null},{"id":"2112.02424","title":"Variational Wasserstein gradient flow","created":"2021-12-04","cited_by_count":53,"area":null},{"id":"2110.14188","title":"RoMA: Robust Model Adaptation for Offline Model-based Optimization","created":"2021-10-27","cited_by_count":39,"area":null},{"id":"2209.13083","title":"Why neural networks find simple solutions: the many regularizers of geometric complexity","created":"2022-09-27","cited_by_count":39,"area":null},{"id":"2106.08929","title":"KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support","created":"2021-06-16","cited_by_count":37,"area":null},{"id":"2101.07496","title":"Disentangled Recurrent Wasserstein Autoencoder","created":"2021-01-19","cited_by_count":22,"area":null},{"id":"2405.06780","title":"Deep MMD Gradient Flow without adversarial training","created":"2024-05-10","cited_by_count":19,"area":null},{"id":"2303.01687","title":"Differentially Private Neural Tangent Kernels for Privacy-Preserving Data Generation","created":"2023-03-03","cited_by_count":15,"area":null},{"id":"2112.00423","title":"Controlling Wasserstein Distances by Kernel Norms with Application to Compressive Statistical Learning","created":"2021-12-01","cited_by_count":14,"area":null},{"id":"2111.15090","title":"The Geometric Occam's Razor Implicit in Deep Learning","created":"2021-11-30","cited_by_count":7,"area":null},{"id":"2605.11884","title":"Sobolev Regularized MMD Gradient Flow","created":"2026-05-12","cited_by_count":6,"area":null},{"id":"2206.06234","title":"Evaluating Graph Generative Models with Contrastively Learned Features","created":"2022-06-13","cited_by_count":5,"area":null},{"id":"2101.07524","title":"DuelGAN: A Duel Between Two Discriminators Stabilizes the GAN Training","created":"2021-01-19","cited_by_count":4,"area":null},{"id":"2102.08474","title":"Adversarially Robust Kernel Smoothing","created":"2021-02-16","cited_by_count":4,"area":null},{"id":"2202.05910","title":"Multi-level Latent Space Structuring for Generative Control","created":"2022-02-11","cited_by_count":4,"area":null},{"id":"2310.02941","title":"Hoeffding's Inequality for Markov Chains under Generalized Concentrability Condition","created":"2023-10-04","cited_by_count":3,"area":null},{"id":"2501.04898","title":"Optimality and Adaptivity of Deep Neural Features for Instrumental Variable Regression","created":"2025-01-09","cited_by_count":3,"area":null},{"id":"2105.04538","title":"Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels","created":"2021-05-10","cited_by_count":2,"area":null},{"id":"2306.00785","title":"Data Interpolants -- That's What Discriminators in Higher-order Gradient-regularized GANs Are","created":"2023-06-01","cited_by_count":2,"area":null},{"id":"2409.06890","title":"Learning Representations for Independence Testing","created":"2024-09-10","cited_by_count":2,"area":null},{"id":"2505.20754","title":"Stationary MMD Points","created":"2025-05-27","cited_by_count":2,"area":null},{"id":"2504.05945","title":"CKGAN: Training Generative Adversarial Networks Using Characteristic Kernel Integral Probability Metrics","created":"2025-04-08","cited_by_count":1,"area":null},{"id":"2307.01879","title":"Stability Analysis Framework for Particle-based Distance GANs with Wasserstein Gradient Flow","created":"2023-07-04","cited_by_count":0,"area":null},{"id":"2505.24469","title":"Smooth Model Compression without Fine-Tuning","created":"2025-05-30","cited_by_count":0,"area":null},{"id":"2506.21731","title":"Exploring Image Generation via Mutually Exclusive Probability Spaces and Local Correlation Hypothesis","created":"2025-06-26","cited_by_count":0,"area":null},{"id":"2508.21531","title":"Adaptive generative moment matching networks for improved learning of dependence structures","created":"2025-08-29","cited_by_count":0,"area":null}],"next_cursor":null}