:: Library Catalog

Imagen de Portada

Guardado en:

Detalles Bibliográficos
Autor principal:	Xu, Xiaoda
Formato:	Preprint
Publicado:	2026
Materias:	Machine Learning Probability
Acceso en línea:	https://arxiv.org/abs/2603.00202
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

On a Class of Partitions with Lower Expected Star Discrepancy and Its Upper Bound than Jittered Sampling
por: Xu, Xiaoda, et al.
Publicado: (2025)

Expected star discrepancy based on stratified sampling
por: Xu, Xiaoda, et al.
Publicado: (2025)

Self-Repellent Random Walks on General Graphs -- Achieving Minimal Sampling Variance via Nonlinear Markov Chains
por: Doshi, Vishwaraj, et al.
Publicado: (2023)

Expected Batch Optimal Transport Plans and Consequences for Flow Matching
por: Boïté, Samuel, et al.
Publicado: (2026)

Learning with Expected Signatures: Theory and Applications
por: Lucchese, Lorenzo, et al.
Publicado: (2025)

Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
por: Altekrüger, Fabian, et al.
Publicado: (2023)

The Gittins Index: A Design Principle for Decision-Making Under Uncertainty
por: Scully, Ziv, et al.
Publicado: (2025)

Equalised Odds is not Equal Individual Odds: Post-processing for Group and Individual Fairness
por: Small, Edward A., et al.
Publicado: (2023)

Stochastic Operator Network: A Stochastic Maximum Principle Based Approach to Operator Learning
por: Bausback, Ryan, et al.
Publicado: (2025)

Beyond Non-Degeneracy: Revisiting Certainty Equivalent Heuristic for Online Linear Programming
por: Chen, Yilun, et al.
Publicado: (2025)

Random uniform approximation under weighted importance sampling of a class of stratified input
por: Xian, Jun, et al.
Publicado: (2025)

Random ReLU Neural Networks as Non-Gaussian Processes
por: Parhi, Rahul, et al.
Publicado: (2024)

Fooling Algorithms in Non-Stationary Bandits using Belief Inertia
por: Mendelson, Gal, et al.
Publicado: (2025)

Non-Asymptotic Convergence of Stochastic Iterative Algorithms: A Lyapunov Framework
por: Chen, Zaiwei, et al.
Publicado: (2026)

Fast Conditional Mixing of MCMC Algorithms for Non-log-concave Distributions
por: Cheng, Xiang, et al.
Publicado: (2023)

A Unifying Perspective on Non-Stationary Kernels for Deeper Gaussian Processes
por: Noack, Marcus M., et al.
Publicado: (2023)

Achieving the Kesten-Stigum bound in the non-uniform hypergraph stochastic block model
por: Fernandez V, Manuel, et al.
Publicado: (2026)

A Learning-Based Superposition Operator for Non-Renewal Arrival Processes in Queueing Networks
por: Sherzer, Eliran
Publicado: (2026)

Non-Reversible Langevin Algorithms for Constrained Sampling
por: Du, Hengrong, et al.
Publicado: (2025)

Bayesian Hierarchical Models and the Maximum Entropy Principle
por: Brewer, Brendon J.
Publicado: (2026)

Variational Kernel Design for Internal Noise: Gaussian Chaos Noise, Representation Compatibility, and Reliable Deep Learning
por: Liu, Ziran
Publicado: (2026)

Adaptive Learning via Off-Model Training and Importance Sampling for Fully Non-Markovian Optimal Stochastic Control. Complete version
por: Leão, Dorival, et al.
Publicado: (2026)

Sparse random hypergraphs: Non-backtracking spectra and community detection
por: Stephan, Ludovic, et al.
Publicado: (2022)

MEP-Net: Generating Solutions to Scientific Problems with Limited Knowledge by Maximum Entropy Principle
por: Yang, Wuyue, et al.
Publicado: (2024)

Non-asymptotic Analysis of Diffusion Annealed Langevin Monte Carlo for Generative Modelling
por: Cordero-Encinar, Paula, et al.
Publicado: (2025)

Kernel Stein Discrepancy on Lie Groups: Theory and Applications
por: Qu, Xiaoda, et al.
Publicado: (2023)

Bayesian Inference with Shaped Deep Non-linear MLPs
por: Hanin, Boris, et al.
Publicado: (2026)

Error estimates between SGD with momentum and underdamped Langevin diffusion
por: Guillin, Arnaud, et al.
Publicado: (2024)

Non-Asymptotic Analysis of Data Augmentation for Precision Matrix Estimation
por: Morisset, Lucas, et al.
Publicado: (2025)

MetaCURL: Non-stationary Concave Utility Reinforcement Learning
por: Moreno, Bianca Marin, et al.
Publicado: (2024)

Non-convex entropic mean-field optimization via Best Response flow
por: Lascu, Razvan-Andrei, et al.
Publicado: (2025)

Designing Algorithms for Entropic Optimal Transport from an Optimisation Perspective
por: Srinivasan, Vishwak, et al.
Publicado: (2025)

PurSAMERE: Reliable Adversarial Purification via Sharpness-Aware Minimization of Expected Reconstruction Error
por: Hoang, Vinh, et al.
Publicado: (2026)

Quantum Reinforcement Learning in Non-Abelian Environments: Unveiling Novel Formulations and Quantum Advantage Exploration
por: Ghosal, Shubhayan
Publicado: (2024)

Convergence Error Analysis of Reflected Gradient Langevin Dynamics for Globally Optimizing Non-Convex Constrained Problems
por: Sato, Kanji, et al.
Publicado: (2022)

Weak Correlations as the Underlying Principle for Linearization of Gradient-Based Learning Systems
por: Shem-Ur, Ori, et al.
Publicado: (2024)

Non-asymptotic estimates for accelerated high order Langevin Monte Carlo algorithms
por: Neufeld, Ariel, et al.
Publicado: (2024)

WHOMP: Optimizing Randomized Controlled Trials via Wasserstein Homogeneity
por: Xu, Shizhou, et al.
Publicado: (2024)

Information-Theoretic Limits and Strong Consistency on Binary Non-uniform Hypergraph Stochastic Block Models
por: Wang, Hai-Xiao
Publicado: (2023)

Zeroth-Order Sampling Methods for Non-Log-Concave Distributions: Alleviating Metastability by Denoising Diffusion
por: He, Ye, et al.
Publicado: (2024)