:: Library Catalog

Cover Image

Saved in:

Bibliographic Details
Main Authors:	Cui, Hugo, Lu, Yue M.
Format:	Preprint
Published:	2026
Subjects:	Machine Learning
Online Access:	https://arxiv.org/abs/2601.23031
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

A Random Matrix Theory Perspective on the Spectrum of Learned Features and Asymptotic Generalization Capabilities
by: Dandi, Yatin, et al.
Published: (2024)

A Differentially Private Weighted Empirical Risk Minimization Procedure and its Application to Outcome Weighted Learning
by: Giddens, Spencer, et al.
Published: (2023)

Empirical Risk Minimization with $f$-Divergence Regularization
by: Daunas, Francisco, et al.
Published: (2026)

Universal Rates of Empirical Risk Minimization
by: Hanneke, Steve, et al.
Published: (2024)

On the Performance of Empirical Risk Minimization with Smoothed Data
by: Block, Adam, et al.
Published: (2024)

Asymmetry of the Relative Entropy in the Regularization of Empirical Risk Minimization
by: Daunas, Francisco, et al.
Published: (2024)

Empirical Risk Minimization with Relative Entropy Regularization
by: Perlaza, Samir M., et al.
Published: (2022)

High-Dimensional Analysis of Single-Layer Attention for Sparse-Token Classification
by: Barnfield, Nicholas, et al.
Published: (2025)

A Researcher's Guide to Empirical Risk Minimization
by: van der Laan, Lars
Published: (2026)

On the Variance, Admissibility, and Stability of Empirical Risk Minimization
by: Kur, Gil, et al.
Published: (2023)

Differentially Private Two-Stage Empirical Risk Minimization with Applications to Individualized Treatment Rule
by: Lee, Joowon, et al.
Published: (2026)

High-dimensional Asymptotics of Denoising Autoencoders
by: Cui, Hugo, et al.
Published: (2023)

Equivalence of the Empirical Risk Minimization to Regularization on the Family of f-Divergences
by: Daunas, Francisco, et al.
Published: (2024)

Transductive Active Learning: Theory and Applications
by: Hübotter, Jonas, et al.
Published: (2024)

Asymptotic Characterisation of Robust Empirical Risk Minimisation Performance in the Presence of Outliers
by: Vilucchio, Matteo, et al.
Published: (2023)

Asymptotics of feature learning in two-layer networks after one gradient-step
by: Cui, Hugo, et al.
Published: (2024)

A Dual Optimization View to Empirical Risk Minimization with f-Divergence Regularization
by: Daunas, Francisco, et al.
Published: (2025)

Optimal Empirical Risk Minimization under Temporal Distribution Shifts
by: Jeong, Yujin, et al.
Published: (2025)

Asymptotics of Learning with Deep Structured (Random) Features
by: Schröder, Dominik, et al.
Published: (2024)

Characterization of Gaussian Universality Breakdown in High-Dimensional Empirical Risk Minimization
by: Yaakoubi, Chiheb, et al.
Published: (2026)

Neural Stochastic Differential Equations on Compact State Spaces: Theory, Methods, and Application to Suicide Risk Modeling
by: Lu, Malinda, et al.
Published: (2025)

Concentration of Non-Isotropic Random Tensors with Applications to Learning and Empirical Risk Minimization
by: Even, Mathieu, et al.
Published: (2021)

A solvable model of learning generative diffusion: theory and insights
by: Cui, Hugo, et al.
Published: (2025)

f-FERM: A Scalable Framework for Robust Fair Empirical Risk Minimization
by: Baharlouei, Sina, et al.
Published: (2023)

Optimal Excess Risk Bounds for Empirical Risk Minimization on $p$-Norm Linear Regression
by: Hanchi, Ayoub El, et al.
Published: (2023)

Using Taylor-Approximated Gradients to Improve the Frank-Wolfe Method for Empirical Risk Minimization
by: Xiong, Zikai, et al.
Published: (2022)

Specifying and Solving Robust Empirical Risk Minimization Problems Using CVXPY
by: Luxenberg, Eric, et al.
Published: (2023)

A Unified Empirical Risk Minimization Framework for Flexible N-Tuples Weak Supervision
by: Huang, Shuying, et al.
Published: (2025)

Exact Instance Compression for Convex Empirical Risk Minimization via Color Refinement
by: Zhu, Bryan, et al.
Published: (2026)

A Non-Asymptotic Theory of Seminorm Lyapunov Stability: From Deterministic to Stochastic Iterative Algorithms
by: Chen, Zaiwei, et al.
Published: (2025)

Statistical Theory of Multi-stage Newton Iteration Algorithm for Online Continual Learning
by: Lu, Xinjia, et al.
Published: (2025)

Generalizability of Neural Networks Minimizing Empirical Risk Based on Expressive Ability
by: Yu, Lijia, et al.
Published: (2025)

$L_2$-Regularized Empirical Risk Minimization Guarantees Small Smooth Calibration Error
by: Fujisawa, Masahiro, et al.
Published: (2025)

Empirical Risk Minimization with Shuffled SGD: A Primal-Dual Perspective and Improved Bounds
by: Cai, Xufeng, et al.
Published: (2023)

Improved Sample Complexity For Diffusion Model Training Without Empirical Risk Minimizer Access
by: Gaur, Mudit, et al.
Published: (2025)

A Lower Bound and a Near-Optimal Algorithm for Bilevel Empirical Risk Minimization
by: Dagréou, Mathieu, et al.
Published: (2023)

Benefits of Online Tilted Empirical Risk Minimization: A Case Study of Outlier Detection and Robust Regression
by: Yildirim, Yigit E., et al.
Published: (2025)

Asymptotics of Random Feature Regression Beyond the Linear Scaling Regime
by: Hu, Hong, et al.
Published: (2024)

An Empirical Risk Minimization Approach for Offline Inverse RL and Dynamic Discrete Choice Model
by: Kang, Enoch H., et al.
Published: (2025)

Muon Converges under Heavy-Tailed Noise: Nonconvex Hölder-Smooth Empirical Risk Minimization
by: Iiduka, Hideaki
Published: (2026)