Cover Image

Statistical Error Bounds for GANs with Nonlinear Objective Functionals

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Birrell, Jeremiah
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

Concentration Inequalities for Stochastic Optimization of Unbounded Objective Functions with Application to Denoising Score Matching
by: Birrell, Jeremiah
Published: (2025)

Learning to Forget with Information Divergence Reweighted Objectives for Noisy Labels
by: Birrell, Jeremiah, et al.
Published: (2025)

Statistical Guarantees for Distributionally Robust Optimization with Optimal Transport and OT-Regularized Divergences
by: Birrell, Jeremiah, et al.
Published: (2026)

Optimal Transport Regularized Divergences: Application to Adversarial Robustness
by: Birrell, Jeremiah, et al.
Published: (2023)

Nonlinear denoising score matching for enhanced learning of structured distributions
by: Birrell, Jeremiah, et al.
Published: (2024)

Differentially Private Stochastic Gradient Descent with Fixed-Size Minibatches: Tighter RDP Guarantees with or without Replacement
by: Birrell, Jeremiah, et al.
Published: (2024)

Information Theoretic Adversarial Training of Large Language Models
by: Zhang, Yiwei, et al.
Published: (2026)

Statistical Guarantees of Group-Invariant GANs
by: Chen, Ziyu, et al.
Published: (2023)

Concentration Inequalities and UQ Bounds for Hypocoercive MCMC Samplers
by: Birrell, Jeremiah, et al.
Published: (2019)

On the regularization of Wasserstein GANs
by: Petzka, Henning, et al.
Published: (2017)

An Interactive Framework for Implementing Privacy-Preserving Federated Learning: Experiments on Large Language Models
by: Ahmadi, Kasra, et al.
Published: (2025)

Finite-Sample Wasserstein Error Bounds and Concentration Inequalities for Nonlinear Stochastic Approximation
by: Kong, Seo Taek, et al.
Published: (2026)

Generalization Error Bounds for Picard-Type Operator Learning in Nonlinear Parabolic PDEs
by: Taniguchi, Koichi, et al.
Published: (2026)

h-calibration: Rethinking Classifier Recalibration with Probabilistic Error-Bounded Objective
by: Huang, Wenjian, et al.
Published: (2025)

Data Cleansing for GANs
by: Terashita, Naoyuki, et al.
Published: (2025)

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

Generalized Regression with Conditional GANs
by: Jobson, Deddy, et al.
Published: (2024)

GANs as Gradient Flows that Converge
by: Huang, Yu-Jui, et al.
Published: (2022)

Generalized Dual Discriminator GANs
by: Chandana, Penukonda Naga, et al.
Published: (2025)

Generative Bayesian Inference with GANs
by: Wang, Yuexi, et al.
Published: (2022)

Double Descent and Other Interpolation Phenomena in GANs
by: Luzi, Lorenzo, et al.
Published: (2021)

Classification Error Bound for Low Bayes Error Conditions in Machine Learning
by: Yang, Zijian, et al.
Published: (2025)

Sample-efficient neural likelihood-free Bayesian inference of implicit HMMs
by: Ghosh, Sanmitra, et al.
Published: (2024)

A New Formulation of Lipschitz Constrained With Functional Gradient Learning for GANs
by: Wan, Chang, et al.
Published: (2025)

Demystifying Diffusion Objectives: Reweighted Losses are Better Variational Bounds
by: Shi, Jiaxin, et al.
Published: (2025)

Finite-Time Error Bounds for Greedy-GQ
by: Wang, Yue, et al.
Published: (2022)

The Exploration of Error Bounds in Classification with Noisy Labels
by: Liu, Haixia, et al.
Published: (2025)

A Differential Equation Approach for Wasserstein GANs and Beyond
by: Malik, Zachariah, et al.
Published: (2024)

Conditional Sig-Wasserstein GANs for Time Series Generation
by: Liao, Shujian, et al.
Published: (2020)

GANs Conditioning Methods: A Survey
by: Bourou, Anis, et al.
Published: (2024)

Worst-case Error Bounds for Online Learning of Smooth Functions
by: Xie, Weian
Published: (2025)

Misspecified $Q$-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error
by: Du, Ally Yalei, et al.
Published: (2024)

PREM: Privately Answering Statistical Queries with Relative Error
by: Ghazi, Badih, et al.
Published: (2025)

Intriguing Properties of Modern GANs
by: Friedman, Roy, et al.
Published: (2024)

Connecting GANs, MFGs, and OT
by: Cao, Haoyang, et al.
Published: (2020)

Tabular GANs for uneven distribution
by: Ashrapov, Insaf
Published: (2020)

Generalization Error Bounds for Learning under Censored Feedback
by: Yang, Yifan, et al.
Published: (2024)

Efficient Distribution Learning with Error Bounds in Wasserstein Distance
by: Figueiredo, Eduardo, et al.
Published: (2026)

Hybrid Quantum-Classical GANs for the Generation of Adversarial Network Flows
by: Paudel, Prateek, et al.
Published: (2026)

Exponential capacity scaling of classical GANs compared to hybrid latent style-based quantum GANs
by: Liepelt, Milan, et al.
Published: (2026)