{"total":22,"rows":[{"id":"2304.07288","title":"Cross-Entropy Loss Functions: Theoretical Analysis and Applications","created":"2023-04-14","cited_by_count":210,"area":null},{"id":"2210.00960","title":"Stability Analysis and Generalization Bounds of Adversarial Training","created":"2022-10-03","cited_by_count":30,"area":null},{"id":"2403.19480","title":"$H$-Consistency Guarantees for Regression","created":"2024-03-28","cited_by_count":21,"area":null},{"id":"2211.14966","title":"Adversarial Rademacher Complexity of Deep Neural Networks","created":"2022-11-27","cited_by_count":18,"area":null},{"id":"2202.05420","title":"A Characterization of Semi-Supervised Adversarially-Robust PAC Learnability","created":"2022-02-11","cited_by_count":13,"area":null},{"id":"2103.09947","title":"Understanding Generalization in Adversarial Training via the Bias-Variance Decomposition","created":"2021-03-17","cited_by_count":10,"area":null},{"id":"2110.11950","title":"Adversarial robustness for latent models: Revisiting the robust-standard accuracies tradeoff","created":"2021-10-22","cited_by_count":8,"area":null},{"id":"2206.12977","title":"Adversarially Robust PAC Learnability of Real-Valued Functions","created":"2022-06-26","cited_by_count":8,"area":null},{"id":"2202.06996","title":"Unlabeled Data Help: Minimax Analysis and Adversarial Robustness","created":"2022-02-14","cited_by_count":4,"area":null},{"id":"2301.13486","title":"Robust Linear Regression: Gradient-descent, Early-stopping, and Beyond","created":"2023-01-31","cited_by_count":4,"area":null},{"id":"2103.15385","title":"Lagrangian Objective Function Leads to Improved Unforeseen Attack Generalization in Adversarial Training","created":"2021-03-29","cited_by_count":3,"area":null},{"id":"2205.12695","title":"Surprises in adversarially-trained linear regression","created":"2022-05-25","cited_by_count":2,"area":null},{"id":"2308.00556","title":"Robust Linear Regression: Phase-Transitions and Precise Tradeoffs for General Norms","created":"2023-08-01","cited_by_count":2,"area":null},{"id":"2309.00771","title":"Non-Asymptotic Bounds for Adversarial Excess Risk under Misspecified Models","created":"2023-09-02","cited_by_count":2,"area":null},{"id":"2410.09402","title":"Minimax rates of convergence for nonparametric regression under adversarial attacks","created":"2024-10-12","cited_by_count":2,"area":null},{"id":"2212.02457","title":"Blessings and Curses of Covariate Shifts: Adversarial Learning Dynamics, Directional Convergence, and Equilibria","created":"2022-12-05","cited_by_count":1,"area":null},{"id":"2212.11209","title":"A Theoretical Study of The Effects of Adversarial Attacks on Sparse Regression","created":"2022-12-21","cited_by_count":1,"area":null},{"id":"2306.12582","title":"Adversarial Training with Generated Data in High-Dimensional Regression: An Asymptotic Study","created":"2023-06-21","cited_by_count":1,"area":null},{"id":"2405.06298","title":"PUMA: margin-based data pruning","created":"2024-05-10","cited_by_count":1,"area":null},{"id":"2505.17356","title":"Adversarial Robustness of Nonparametric Regression","created":"2025-05-23","cited_by_count":1,"area":null},{"id":"2601.16070","title":"On damage of interpolation to adversarial robustness in regression","created":"2026-01-22","cited_by_count":1,"area":null},{"id":"2601.18546","title":"Information Hidden in Gradients of Regression with Target Noise","created":"2026-01-26","cited_by_count":0,"area":null}],"next_cursor":null}