{"total":19,"rows":[{"id":"2210.06313","title":"The Lazy Neuron Phenomenon: On Emergence of Activation Sparsity in Transformers","created":"2022-10-12","cited_by_count":122,"area":null},{"id":"2202.05928","title":"Benign Overfitting without Linearity: Neural Network Classifiers Trained by Gradient Descent for Noisy Linear Data","created":"2022-02-11","cited_by_count":74,"area":null},{"id":"2109.02355","title":"A Farewell to the Bias-Variance Tradeoff? An Overview of the Theory of Overparameterized Machine Learning","created":"2021-09-06","cited_by_count":57,"area":null},{"id":"2106.09276","title":"Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds, and Benign Overfitting","created":"2021-06-17","cited_by_count":54,"area":null},{"id":"2110.02914","title":"Foolish Crowds Support Benign Overfitting","created":"2021-10-06","cited_by_count":24,"area":null},{"id":"2203.03597","title":"Fast Rates for Noisy Interpolation Require Rethinking the Effects of Inductive Bias","created":"2022-03-07","cited_by_count":22,"area":null},{"id":"2108.11489","title":"The Interplay Between Implicit Bias and Benign Overfitting in Two-Layer Linear Networks","created":"2021-08-25","cited_by_count":18,"area":null},{"id":"2111.05987","title":"Tight bounds for minimum l1-norm interpolation of noisy data","created":"2021-11-10","cited_by_count":16,"area":null},{"id":"2210.12082","title":"A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models","created":"2022-10-21","cited_by_count":11,"area":null},{"id":"2209.09315","title":"Deep Linear Networks can Benignly Overfit when Shallow Ones Do","created":"2022-09-19","cited_by_count":8,"area":null},{"id":"2110.09502","title":"Minimum $\\ell_{1}$-norm interpolators: Precise asymptotics and multiple descent","created":"2021-10-18","cited_by_count":7,"area":null},{"id":"2302.00257","title":"Implicit Regularization Leads to Benign Overfitting for Sparse Linear Regression","created":"2023-02-01","cited_by_count":4,"area":null},{"id":"2510.15337","title":"Transfer Learning for Benign Overfitting in High-Dimensional Linear Regression","created":"2025-10-17","cited_by_count":3,"area":null},{"id":"2105.02083","title":"AdaBoost and robust one-bit compressed sensing","created":"2021-05-05","cited_by_count":2,"area":null},{"id":"2106.04795","title":"Nonasymptotic theory for two-layer neural networks: Beyond the bias-variance trade-off","created":"2021-06-09","cited_by_count":2,"area":null},{"id":"2603.28956","title":"Minimum Norm Interpolation via the Local Theory of Banach Spaces: The Role of $2$-Uniform Convexity","created":"2026-03-30","cited_by_count":2,"area":null},{"id":"2305.12883","title":"Prediction Risk and Estimation Risk of the Ridgeless Least Squares Estimator under General Assumptions on Regression Errors","created":"2023-05-22","cited_by_count":0,"area":null},{"id":"2409.00733","title":"Benign Overfitting under Learning Rate Conditions for $\\alpha$ Sub-exponential Input","created":"2024-09-01","cited_by_count":0,"area":null},{"id":"2609.33675","title":"Benign Overfitting for General Norms and Distributions","created":"2026-09-27","cited_by_count":0,"area":null}],"next_cursor":null}