{"total":185,"rows":[{"id":"2201.12220","title":"Neural Optimal Transport","created":"2022-01-28","cited_by_count":126,"area":null},{"id":"2106.01954","title":"Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark","created":"2021-06-03","cited_by_count":96,"area":null},{"id":"2106.06345","title":"Proximal Optimal Transport Modeling of Population Dynamics","created":"2021-06-11","cited_by_count":93,"area":null},{"id":"2109.12004","title":"Entropic estimation of optimal transport maps","created":"2021-09-24","cited_by_count":90,"area":null},{"id":"2202.00665","title":"Tutorial on amortized optimization","created":"2022-02-01","cited_by_count":83,"area":null},{"id":"2110.02999","title":"Generative Modeling with Optimal Transport Maps","created":"2021-10-06","cited_by_count":82,"area":null},{"id":"2106.00736","title":"Large-Scale Wasserstein Gradient Flows","created":"2021-06-01","cited_by_count":77,"area":null},{"id":"2210.01741","title":"Neural Conservation Laws: A Divergence-Free Perspective","created":"2022-10-04","cited_by_count":70,"area":null},{"id":"2107.12364","title":"Plugin Estimation of Smooth Optimal Transport Maps","created":"2021-07-26","cited_by_count":68,"area":null},{"id":"2103.04737","title":"Low-Rank Sinkhorn Factorization","created":"2021-03-08","cited_by_count":67,"area":null},{"id":"2306.16156","title":"Recent Advances in Optimal Transport for Machine Learning","created":"2023-06-28","cited_by_count":66,"area":null},{"id":"2206.14262","title":"Supervised Training of Conditional Monge Maps","created":"2022-06-28","cited_by_count":61,"area":null},{"id":"2301.00537","title":"Posterior Collapse and Latent Variable Non-identifiability","created":"2023-01-02","cited_by_count":57,"area":null},{"id":"2106.00774","title":"Optimizing Functionals on the Space of Probabilities with Input Convex Neural Networks","created":"2021-06-01","cited_by_count":53,"area":null},{"id":"2112.02424","title":"Variational Wasserstein gradient flow","created":"2021-12-04","cited_by_count":53,"area":null},{"id":"2102.01752","title":"Continuous Wasserstein-2 Barycenter Estimation without Minimax Optimization","created":"2021-02-02","cited_by_count":51,"area":null},{"id":"2211.01156","title":"Entropic Neural Optimal Transport via Diffusion Processes","created":"2022-11-02","cited_by_count":50,"area":null},{"id":"2310.10649","title":"A Computational Framework for Solving Wasserstein Lagrangian Flows","created":"2023-10-16","cited_by_count":46,"area":null},{"id":"2108.09645","title":"Improving Mini-batch Optimal Transport via Partial Transportation","created":"2021-08-22","cited_by_count":41,"area":null},{"id":"2305.14777","title":"Generative Modeling through the Semi-dual Formulation of Unbalanced Optimal Transport","created":"2023-05-24","cited_by_count":41,"area":null},{"id":"2106.03812","title":"Neural Monge Map estimation and its applications","created":"2021-06-07","cited_by_count":40,"area":null},{"id":"2205.15403","title":"Neural Optimal Transport with General Cost Functionals","created":"2022-05-30","cited_by_count":36,"area":null},{"id":"2311.15100","title":"Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation","created":"2023-11-25","cited_by_count":33,"area":null},{"id":"2302.04953","title":"The Monge Gap: A Regularizer to Learn All Transport Maps","created":"2023-02-09","cited_by_count":32,"area":null},{"id":"2205.15269","title":"Kernel Neural Optimal Transport","created":"2022-05-30","cited_by_count":31,"area":null},{"id":"2210.12153","title":"On amortizing convex conjugates for optimal transport","created":"2022-10-21","cited_by_count":31,"area":null},{"id":"2206.05262","title":"Meta Optimal Transport","created":"2022-06-10","cited_by_count":28,"area":null},{"id":"2111.01361","title":"Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysis","created":"2021-11-02","cited_by_count":27,"area":null},{"id":"2305.05080","title":"Scalable Optimal Transport Methods in Machine Learning: A Contemporary Survey","created":"2023-05-08","cited_by_count":27,"area":null},{"id":"2405.08719","title":"Addressing Misspecification in Simulation-based Inference through Data-driven Calibration","created":"2024-05-14","cited_by_count":27,"area":null},{"id":"2210.10275","title":"Towards Explaining Distribution Shifts","created":"2022-10-19","cited_by_count":26,"area":null},{"id":"2304.06094","title":"Energy-guided Entropic Neural Optimal Transport","created":"2023-04-12","cited_by_count":26,"area":null},{"id":"2311.05672","title":"Conditional Optimal Transport on Function Spaces","created":"2023-11-09","cited_by_count":26,"area":null},{"id":"2310.14344","title":"What's in a Prior? Learned Proximal Networks for Inverse Problems","created":"2023-10-22","cited_by_count":25,"area":null},{"id":"2404.04240","title":"Dynamic Conditional Optimal Transport through Simulation-Free Flows","created":"2024-04-05","cited_by_count":25,"area":null},{"id":"2201.12245","title":"Wasserstein Iterative Networks for Barycenter Estimation","created":"2022-01-28","cited_by_count":23,"area":null},{"id":"2212.03722","title":"Optimal transport map estimation in general function spaces","created":"2022-12-07","cited_by_count":20,"area":null},{"id":"2202.11316","title":"Multivariate Quantile Function Forecaster","created":"2022-02-23","cited_by_count":19,"area":null},{"id":"2306.10161","title":"Building the Bridge of Schr\\\"odinger: A Continuous Entropic Optimal Transport Benchmark","created":"2023-06-16","cited_by_count":19,"area":null},{"id":"2310.01174","title":"Light Schr\\\"odinger Bridge","created":"2023-10-02","cited_by_count":18,"area":null},{"id":"2106.10272","title":"Riemannian Convex Potential Maps","created":"2021-06-18","cited_by_count":17,"area":null},{"id":"2111.12187","title":"Input Convex Gradient Networks","created":"2021-11-23","cited_by_count":17,"area":null},{"id":"2209.15621","title":"Neural Unbalanced Optimal Transport via Cycle-Consistent Semi-Couplings","created":"2022-09-30","cited_by_count":17,"area":null},{"id":"2311.11475","title":"Gaussian Interpolation Flows","created":"2023-11-20","cited_by_count":17,"area":null},{"id":"2102.05912","title":"On Transportation of Mini-batches: A Hierarchical Approach","created":"2021-02-11","cited_by_count":16,"area":null},{"id":"2302.04065","title":"Monge, Bregman and Occam: Interpretable Optimal Transport in High-Dimensions with Feature-Sparse Maps","created":"2023-02-08","cited_by_count":16,"area":null},{"id":"2305.16557","title":"Tree-Based Diffusion Schr\\\"odinger Bridge with Applications to Wasserstein Barycenters","created":"2023-05-26","cited_by_count":16,"area":null},{"id":"2406.00288","title":"Neural Optimal Transport with Lagrangian Costs","created":"2024-06-01","cited_by_count":16,"area":null},{"id":"2111.13236","title":"Joint inference and input optimization in equilibrium networks","created":"2021-11-25","cited_by_count":15,"area":null},{"id":"2306.02342","title":"Deep Optimal Transport: A Practical Algorithm for Photo-realistic Image Restoration","created":"2023-06-04","cited_by_count":15,"area":null},{"id":"2402.03828","title":"Estimating Barycenters of Distributions with Neural Optimal Transport","created":"2024-02-06","cited_by_count":14,"area":null},{"id":"2404.09411","title":"Wasserstein Wormhole: Scalable Optimal Transport Distance with Transformers","created":"2024-04-15","cited_by_count":14,"area":null},{"id":"2112.01907","title":"Near-optimal estimation of smooth transport maps with kernel sums-of-squares","created":"2021-12-03","cited_by_count":12,"area":null},{"id":"2301.12874","title":"Extremal Domain Translation with Neural Optimal Transport","created":"2023-01-30","cited_by_count":12,"area":null},{"id":"2310.01105","title":"Energy-Guided Continuous Entropic Barycenter Estimation for General Costs","created":"2023-10-02","cited_by_count":12,"area":null},{"id":"2312.12474","title":"Principled Weight Initialisation for Input-Convex Neural Networks","created":"2023-12-19","cited_by_count":12,"area":null},{"id":"2203.11869","title":"An Optimal Transport Formulation of Bayes' Law for Nonlinear Filtering Algorithms","created":"2022-03-22","cited_by_count":11,"area":null},{"id":"2310.13886","title":"Nonlinear Filtering with Brenier Optimal Transport Maps","created":"2023-10-21","cited_by_count":11,"area":null},{"id":"2401.12253","title":"Accelerating Sinkhorn Algorithm with Sparse Newton Iterations","created":"2024-01-20","cited_by_count":11,"area":null},{"id":"2405.12888","title":"Keep the Momentum: Conservation Laws beyond Euclidean Gradient Flows","created":"2024-05-21","cited_by_count":11,"area":null},{"id":"2102.02992","title":"Learning High Dimensional Wasserstein Geodesics","created":"2021-02-05","cited_by_count":10,"area":null},{"id":"2109.14090","title":"Reversible Gromov-Monge Sampler for Simulation-Based Inference","created":"2021-09-28","cited_by_count":10,"area":null},{"id":"2410.03974","title":"Robust Barycenter Estimation using Semi-Unbalanced Neural Optimal Transport","created":"2024-10-04","cited_by_count":9,"area":null},{"id":"2503.03025","title":"Hierarchical Refinement: Optimal Transport to Infinity and Beyond","created":"2025-03-04","cited_by_count":9,"area":null},{"id":"2302.01237","title":"Robust Estimation under the Wasserstein Distance","created":"2023-02-02","cited_by_count":8,"area":null},{"id":"2302.01961","title":"Asymmetric Certified Robustness via Feature-Convex Neural Networks","created":"2023-02-03","cited_by_count":8,"area":null},{"id":"2303.07988","title":"Light Unbalanced Optimal Transport","created":"2023-03-14","cited_by_count":8,"area":null},{"id":"2305.11857","title":"Computing high-dimensional optimal transport by flow neural networks","created":"2023-05-19","cited_by_count":8,"area":null},{"id":"2310.02611","title":"Analyzing and Improving Optimal-Transport-based Adversarial Networks","created":"2023-10-04","cited_by_count":8,"area":null},{"id":"2102.03895","title":"Functional optimal transport: map estimation and domain adaptation for functional data","created":"2021-02-07","cited_by_count":7,"area":null},{"id":"2301.10862","title":"Learning Gradients of Convex Functions with Monotone Gradient Networks","created":"2023-01-25","cited_by_count":7,"area":null},{"id":"2307.04102","title":"A generative flow for conditional sampling via optimal transport","created":"2023-07-09","cited_by_count":7,"area":null},{"id":"2410.03783","title":"Improving Neural Optimal Transport via Displacement Interpolation","created":"2024-10-03","cited_by_count":7,"area":null},{"id":"2202.04732","title":"Online Learning to Transport via the Minimal Selection Principle","created":"2022-02-09","cited_by_count":6,"area":null},{"id":"2209.10873","title":"Turning Normalizing Flows into Monge Maps with Geodesic Gaussian Preserving Flows","created":"2022-09-22","cited_by_count":6,"area":null},{"id":"2303.05978","title":"Uncovering Challenges of Solving the Continuous Gromov-Wasserstein Problem","created":"2023-03-10","cited_by_count":6,"area":null},{"id":"2306.11895","title":"Learning Elastic Costs to Shape Monge Displacements","created":"2023-06-20","cited_by_count":6,"area":null},{"id":"2403.03777","title":"ENOT: Expectile Regularization for Fast and Accurate Training of Neural Optimal Transport","created":"2024-03-06","cited_by_count":6,"area":null},{"id":"2501.03017","title":"Convexity in ReLU Neural Networks: beyond ICNNs?","created":"2025-01-06","cited_by_count":6,"area":null},{"id":"2502.04583","title":"Overcoming Spurious Solutions in Semi-Dual Neural Optimal Transport: A Smoothing Approach for Learning the Optimal Transport Plan","created":"2025-02-07","cited_by_count":6,"area":null},{"id":"2107.08648","title":"Transport away your problems: Calibrating stochastic simulations with optimal transport","created":"2021-07-19","cited_by_count":5,"area":null},{"id":"2111.11306","title":"Learning PSD-valued functions using kernel sums-of-squares","created":"2021-11-22","cited_by_count":5,"area":null},{"id":"2111.11328","title":"Cycle Consistent Probability Divergences Across Different Spaces","created":"2021-11-22","cited_by_count":5,"area":null},{"id":"2205.07521","title":"A scalable deep learning approach for solving high-dimensional dynamic optimal transport","created":"2022-05-16","cited_by_count":5,"area":null},{"id":"2305.16846","title":"Lagrangian Flow Networks for Conservation Laws","created":"2023-05-26","cited_by_count":5,"area":null},{"id":"2306.06866","title":"Generating Synthetic Datasets by Interpolating along Generalized Geodesics","created":"2023-06-12","cited_by_count":5,"area":null},{"id":"2505.21208","title":"Input Convex Kolmogorov Arnold Networks","created":"2025-05-27","cited_by_count":5,"area":null},{"id":"2202.07965","title":"GAN Estimation of Lipschitz Optimal Transport Maps","created":"2022-02-16","cited_by_count":4,"area":null},{"id":"2207.14727","title":"Tangential Wasserstein Projections","created":"2022-07-29","cited_by_count":4,"area":null},{"id":"2308.00273","title":"Neural approximation of Wasserstein distance via a universal architecture for symmetric and factorwise group invariant functions","created":"2023-08-01","cited_by_count":4,"area":null},{"id":"2311.08972","title":"Unsupervised approaches based on optimal transport and convex analysis for inverse problems in imaging","created":"2023-11-15","cited_by_count":4,"area":null},{"id":"2403.00147","title":"Analysis of Kernel Mirror Prox for Measure Optimization","created":"2024-02-29","cited_by_count":4,"area":null},{"id":"2405.10302","title":"Optimal Aggregation of Prediction Intervals under Unsupervised Domain Shift","created":"2024-05-16","cited_by_count":4,"area":null},{"id":"2411.00614","title":"Fast and scalable Wasserstein-1 neural optimal transport solver for single-cell perturbation prediction","created":"2024-11-01","cited_by_count":4,"area":null},{"id":"2502.12793","title":"Unsupervised Anomaly Detection through Mass Repulsing Optimal Transport","created":"2025-02-18","cited_by_count":4,"area":null},{"id":"2503.21673","title":"A friendly introduction to triangular transport","created":"2025-03-27","cited_by_count":4,"area":null},{"id":"2604.04342","title":"Generative models for decision-making under distributional shift","created":"2026-04-06","cited_by_count":4,"area":null},{"id":"2203.12595","title":"PhysioMTL: Personalizing Physiological Patterns using Optimal Transport Multi-Task Regression","created":"2022-03-19","cited_by_count":3,"area":null},{"id":"2302.02092","title":"Interpolation for Robust Learning: Data Augmentation on Wasserstein Geodesics","created":"2023-02-04","cited_by_count":3,"area":null},{"id":"2310.18371","title":"In-Context Ability Transfer for Question Decomposition in Complex QA","created":"2023-10-26","cited_by_count":3,"area":null}],"next_cursor":"100"}