Cover Image

Differentiable Cost-Parameterized Monge Map Estimators

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Howard, Samuel, Deligiannidis, George, Rebeschini, Patrick, Thornton, James
Format:	Preprint
Published:	2024
Subjects:	Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Schrödinger Bridge Matching for Tree-Structured Costs and Entropic Wasserstein Barycentres
by: Howard, Samuel, et al.
Published: (2025)

Implicit Regularisation in Diffusion Models: An Algorithm-Dependent Generalisation Analysis
by: Farghly, Tyler, et al.
Published: (2025)

Drift Estimation for Stochastic Differential Equations with Denoising Diffusion Models
by: Costa, Marcos Tapia, et al.
Published: (2026)

A Novel Framework for Policy Mirror Descent with General Parameterization and Linear Convergence
by: Alfano, Carlo, et al.
Published: (2023)

Stochastic Shortest Path with Sparse Adversarial Costs
by: Johnson, Emmeran, et al.
Published: (2025)

Robust Gradient Descent for Phase Retrieval
by: Buna, Alex, et al.
Published: (2024)

Diffusion Models and the Manifold Hypothesis: Log-Domain Smoothing is Geometry Adaptive
by: Farghly, Tyler, et al.
Published: (2025)

Best-of-Both Worlds for linear contextual bandits with paid observations
by: Boyer, Nathan, et al.
Published: (2025)

Sharp Risk Bounds for Early-Stopping in Gaussian Linear Regression
by: Wegel, Tobias, et al.
Published: (2025)

Error Bounds for Flow Matching Methods
by: Benton, Joe, et al.
Published: (2023)

On the Expected Size of Conformal Prediction Sets
by: Dhillon, Guneet S., et al.
Published: (2023)

Learning Elastic Costs to Shape Monge Displacements
by: Klein, Michal, et al.
Published: (2023)

Sample-Efficiency in Multi-Batch Reinforcement Learning: The Need for Dimension-Dependent Adaptivity
by: Johnson, Emmeran, et al.
Published: (2023)

Convergence of Diffusion Models Under the Manifold Hypothesis in High-Dimensions
by: Azangulov, Iskander, et al.
Published: (2024)

Linear Convergence of Diffusion Models Under the Manifold Hypothesis
by: Potaptchik, Peter, et al.
Published: (2024)

Self-Concordant Perturbations for Linear Bandits
by: Lévy, Lucas, et al.
Published: (2025)

Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets
by: Dupuis, Benjamin, et al.
Published: (2024)

Generalization Bounds for Markov Algorithms through Entropy Flow Computations
by: Dupuis, Benjamin, et al.
Published: (2025)

Ranking In Generalized Linear Bandits
by: Shidani, Amitis, et al.
Published: (2022)

On the necessity of adaptive regularisation:Optimal anytime online learning on $\boldsymbol{\ell_p}$-balls
by: Johnson, Emmeran, et al.
Published: (2025)

Non-stationary Bandit Convex Optimization: A Comprehensive Study
by: Liu, Xiaoqi, et al.
Published: (2025)

Nearly $d$-Linear Convergence Bounds for Diffusion Models via Stochastic Localization
by: Benton, Joe, et al.
Published: (2023)

Neural Score Matching for High-Dimensional Causal Inference
by: Clivio, Oscar, et al.
Published: (2022)

Beyond Real Data: Synthetic Data through the Lens of Regularization
by: Shidani, Amitis, et al.
Published: (2025)

Generalisation under gradient descent via deterministic PAC-Bayes
by: Clerico, Eugenio, et al.
Published: (2022)

Stability of the Monge Map in Semi-Dual Optimal Transport
by: Selitskiy, Anton, et al.
Published: (2026)

Learning mirror maps in policy mirror descent
by: Alfano, Carlo, et al.
Published: (2024)

Convolutional Monge Mapping between EEG Datasets to Support Independent Component Labeling
by: Meek, Austin, et al.
Published: (2025)

From Denoising Diffusions to Denoising Markov Models
by: Benton, Joe, et al.
Published: (2022)

Rao-Blackwellised Reparameterisation Gradients
by: Lam, Kevin H., et al.
Published: (2025)

Algebraic Diversity: Group-Theoretic Spectral Estimation from Single Observations
by: Thornton, Mitchell A.
Published: (2026)

Adaptive Diffusion Guidance via Stochastic Optimal Control
by: Azangulov, Iskander, et al.
Published: (2025)

Meta-Learning Objectives for Preference Optimization
by: Alfano, Carlo, et al.
Published: (2024)

Physically Parameterized Differentiable MUSIC for DoA Estimation with Uncalibrated Arrays
by: Chatelier, Baptiste, et al.
Published: (2024)

Differentiable Chemistry in PINNs for Solving Parameterized and Stiff Reaction Systems
by: Babić, Miloš, et al.
Published: (2026)

Black-Box Uniform Stability for Non-Euclidean Empirical Risk Minimization
by: Vary, Simon, et al.
Published: (2024)

Conditioning Diffusions Using Malliavin Calculus
by: Pidstrigach, Jakiw, et al.
Published: (2025)

Unbalancedness in Neural Monge Maps Improves Unpaired Domain Translation
by: Eyring, Luca, et al.
Published: (2023)

Unconventional Computing based on Four Wave Mixing in Highly Nonlinear Waveguides
by: Sozos, Kostas, et al.
Published: (2024)

TRYLOCK: Defense-in-Depth Against LLM Jailbreaks via Layered Preference and Representation Engineering
by: Thornton, Scott
Published: (2026)