{"total":15,"rows":[{"id":"2311.18817","title":"Dichotomy of Early and Late Phase Implicit Biases Can Provably Induce Grokking","created":"2023-11-30","cited_by_count":80,"area":null},{"id":"2211.14966","title":"Adversarial Rademacher Complexity of Deep Neural Networks","created":"2022-11-27","cited_by_count":18,"area":null},{"id":"2201.09785","title":"Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search","created":"2022-01-24","cited_by_count":17,"area":null},{"id":"2209.05150","title":"Bounding the Rademacher Complexity of Fourier neural operators","created":"2022-09-12","cited_by_count":16,"area":null},{"id":"2305.02252","title":"An Adaptive Algorithm for Learning with Unknown Distribution Drift","created":"2023-05-03","cited_by_count":16,"area":null},{"id":"2306.03962","title":"PILLAR: How to make semi-private learning more effective","created":"2023-06-06","cited_by_count":10,"area":null},{"id":"2404.17442","title":"Uniform Generalization Bounds on Data-Dependent Hypothesis Sets via PAC-Bayesian Theory on Random Sets","created":"2024-04-26","cited_by_count":9,"area":null},{"id":"2104.00322","title":"Domain Invariant Adversarial Learning","created":"2021-04-01","cited_by_count":7,"area":null},{"id":"2102.05134","title":"Local and Global Uniform Convexity Conditions","created":"2021-02-09","cited_by_count":4,"area":null},{"id":"2504.20430","title":"Learning Laplacian Positional Encodings for Heterophilous Graphs","created":"2025-04-29","cited_by_count":4,"area":null},{"id":"2602.02358","title":"Transfer Learning Through Conditional Quantile Matching","created":"2026-02-02","cited_by_count":1,"area":null},{"id":"2402.03576","title":"Generalization Properties of Adversarial Training for $\\ell_0$-Bounded Adversarial Attacks","created":"2024-02-05","cited_by_count":0,"area":null},{"id":"2410.05458","title":"Testing Credibility of Public and Private Surveys through the Lens of Regression","created":"2024-10-07","cited_by_count":0,"area":null},{"id":"2506.11357","title":"Generalization Bound of Gradient Flow through Training Trajectory and Data-dependent Kernel","created":"2025-06-12","cited_by_count":0,"area":null},{"id":"2508.20616","title":"Dimension Agnostic Testing of Survey Data Credibility through the Lens of Regression","created":"2025-08-28","cited_by_count":0,"area":null}],"next_cursor":null}