Cover Image

A New Perspective On Denoising Based On Optimal Transport

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Trillos, Nicolas Garcia, Sen, Bodhisattva
Format:	Preprint
Published:	2023
Subjects:	Statistics Theory Machine Learning Optimization and Control
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Denoising Diffusions with Optimal Transport: Localization, Curvature, and Multi-Scale Complexity
by: Liang, Tengyuan, et al.
Published: (2024)

Wasserstein-Cramér-Rao Theory of Unbiased Estimation
by: Trillos, Nicolás García, et al.
Published: (2025)

Error Analysis of Triangular Optimal Transport Maps for Filtering
by: Al-Jarrah, Mohammad, et al.
Published: (2025)

Online Inference of Constrained Optimization: Primal-Dual Optimality and Sequential Quadratic Programming
by: Gao, Yihang, et al.
Published: (2025)

Variational Transport: A Convergent Particle-BasedAlgorithm for Distributional Optimization
by: Yang, Zhuoran, et al.
Published: (2020)

On Efficient and Scalable Computation of the Nonparametric Maximum Likelihood Estimator in Mixture Models
by: Zhang, Yangjing, et al.
Published: (2022)

An Optimal Transport Approach for Computing Adversarial Training Lower Bounds in Multiclass Classification
by: Trillos, Nicolas Garcia, et al.
Published: (2024)

The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification
by: Trillos, Nicolas Garcia, et al.
Published: (2022)

Stochastic Optimization with Optimal Importance Sampling
by: Aolaritei, Liviu, et al.
Published: (2025)

Learning an Optimal Assortment Policy under Observational Data
by: Han, Yuxuan, et al.
Published: (2025)

Learning and Decision-Making with Data: Optimal Formulations and Phase Transitions
by: Bennouna, Amine, et al.
Published: (2021)

An Elementary Proof of the Near Optimality of LogSumExp Smoothing
by: Samakhoana, Thabo, et al.
Published: (2025)

Lower Bounds on Adversarial Robustness for Multiclass Classification with General Loss Functions
by: Trillos, Camilo Andrés García, et al.
Published: (2025)

Is Q-Learning Minimax Optimal? A Tight Sample Complexity Analysis
by: Li, Gen, et al.
Published: (2021)

A connection between Tempering and Entropic Mirror Descent
by: Chopin, Nicolas, et al.
Published: (2023)

Optimal transport natural gradient for statistical manifolds with continuous sample space
by: Chen, Yifan, et al.
Published: (2018)

Optimal and instance-dependent guarantees for Markovian linear stochastic approximation
by: Mou, Wenlong, et al.
Published: (2021)

4+3 Phases of Compute-Optimal Neural Scaling Laws
by: Paquette, Elliot, et al.
Published: (2024)

High-dimensional Limit of SGD for Diagonal Linear Networks
by: Malaxechebarría, Begoña García, et al.
Published: (2026)

Markov Kernels, Distances and Optimal Control: A Parable of Linear Quadratic Non-Gaussian Distribution Steering
by: Teter, Alexis M. H., et al.
Published: (2025)

The High Line: Exact Risk and Learning Rate Curves of Stochastic Adaptive Learning Rate Algorithms
by: Collins-Woodfin, Elizabeth, et al.
Published: (2024)

Derivative-Free Sequential Quadratic Programming for Equality-Constrained Stochastic Optimization
by: Na, Sen
Published: (2025)

Optimal Regularization for a Data Source
by: Leong, Oscar, et al.
Published: (2022)

A Theory of Feature Learning in Kernel Models
by: Chen, Yunlu, et al.
Published: (2023)

A Spectral Framework for Closed-Form Relative Density Estimation
by: Bach, Francis
Published: (2026)

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

A review of NMF, PLSA, LBA, EMA, and LCA with a focus on the identifiability issue
by: Qi, Qianqian, et al.
Published: (2025)

Computation of Least Trimmed Squares: A Branch-and-Bound framework with Hyperplane Arrangement Enhancements
by: Meng, Xiang, et al.
Published: (2026)

Learning the Uncertainty Sets for Control Dynamics via Set Membership: A Non-Asymptotic Analysis
by: Li, Yingying, et al.
Published: (2023)

Optimal Regularization Under Uncertainty: Distributional Robustness and Convexity Constraints
by: Leong, Oscar, et al.
Published: (2025)

Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model
by: Li, Gen, et al.
Published: (2020)

Convergence of flow-based generative models via proximal gradient descent in Wasserstein space
by: Cheng, Xiuyuan, et al.
Published: (2023)

Algorithms for mean-field variational inference via polyhedral optimization in the Wasserstein space
by: Jiang, Yiheng, et al.
Published: (2023)

Certified Multi-Fidelity Zeroth-Order Optimization
by: de Montbrun, Étienne, et al.
Published: (2023)

High-probability Convergence Bounds for Nonlinear Stochastic Gradient Descent Under Heavy-tailed Noise
by: Armacki, Aleksandar, et al.
Published: (2023)

Bayesian Design Principles for Frequentist Sequential Learning
by: Xu, Yunbei, et al.
Published: (2023)

Joint learning of a network of linear dynamical systems via total variation penalization
by: Donnat, Claire, et al.
Published: (2025)

Pessimism Meets Risk: Risk-Sensitive Offline Reinforcement Learning
by: Zhang, Dake, et al.
Published: (2024)

Mixing Times and Privacy Analysis for the Projected Langevin Algorithm under a Modulus of Continuity
by: Bravo, Mario, et al.
Published: (2025)

Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria
by: Liang, Tengyuan
Published: (2022)