{"total":153,"rows":[{"id":"2105.15004","title":"Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime","created":"2021-05-31","cited_by_count":96,"area":null},{"id":"2310.08391","title":"How Many Pretraining Tasks Are Needed for In-Context Learning of Linear Regression?","created":"2023-10-12","cited_by_count":96,"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":"2103.12692","title":"Benign Overfitting of Constant-Stepsize SGD for Linear Regression","created":"2021-03-23","cited_by_count":71,"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":"2202.05834","title":"Predicting Out-of-Distribution Error with the Projection Norm","created":"2022-02-11","cited_by_count":50,"area":null},{"id":"2104.13628","title":"Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures","created":"2021-04-28","cited_by_count":49,"area":null},{"id":"2402.18819","title":"Dual Operating Modes of In-Context Learning","created":"2024-02-29","cited_by_count":43,"area":null},{"id":"2106.10865","title":"Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation","created":"2021-06-21","cited_by_count":42,"area":null},{"id":"2110.06198","title":"Last Iterate Risk Bounds of SGD with Decaying Stepsize for Overparameterized Linear Regression","created":"2021-10-12","cited_by_count":38,"area":null},{"id":"2207.06569","title":"Benign, Tempered, or Catastrophic: A Taxonomy of Overfitting","created":"2022-07-14","cited_by_count":36,"area":null},{"id":"2108.04552","title":"The Benefits of Implicit Regularization from SGD in Least Squares Problems","created":"2021-08-10","cited_by_count":32,"area":null},{"id":"2303.01462","title":"Benign Overfitting in Linear Classifiers and Leaky ReLU Networks from KKT Conditions for Margin Maximization","created":"2023-03-02","cited_by_count":29,"area":null},{"id":"2205.07069","title":"Homogenization of SGD in high-dimensions: Exact dynamics and generalization properties","created":"2022-05-14","cited_by_count":27,"area":null},{"id":"2208.01857","title":"The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift","created":"2022-08-03","cited_by_count":26,"area":null},{"id":"2110.02914","title":"Foolish Crowds Support Benign Overfitting","created":"2021-10-06","cited_by_count":24,"area":null},{"id":"2210.05021","title":"The good, the bad and the ugly sides of data augmentation: An implicit spectral regularization perspective","created":"2022-10-10","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":"2410.18837","title":"High-dimensional Analysis of Knowledge Distillation: Weak-to-Strong Generalization and Scaling Laws","created":"2024-10-24","cited_by_count":22,"area":"distill"},{"id":"2308.13431","title":"Six Lectures on Linearized Neural Networks","created":"2023-08-25","cited_by_count":20,"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":"2311.14646","title":"More is Better in Modern Machine Learning: when Infinite Overparameterization is Optimal and Overfitting is Obligatory","created":"2023-11-24","cited_by_count":18,"area":null},{"id":"2107.01641","title":"A Theoretical Analysis of Fine-tuning with Linear Teachers","created":"2021-07-04","cited_by_count":17,"area":null},{"id":"2112.04470","title":"Optimistic Rates: A Unifying Theory for Interpolation Learning and Regularization in Linear Regression","created":"2021-12-08","cited_by_count":17,"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":"2203.08564","title":"An elementary analysis of ridge regression with random design","created":"2022-03-16","cited_by_count":15,"area":null},{"id":"2111.05198","title":"Harmless interpolation in regression and classification with structured features","created":"2021-11-09","cited_by_count":14,"area":null},{"id":"2202.01243","title":"Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference","created":"2022-02-02","cited_by_count":14,"area":"safety"},{"id":"2408.04532","title":"How Transformers Utilize Multi-Head Attention in In-Context Learning? A Case Study on Sparse Linear Regression","created":"2024-08-08","cited_by_count":14,"area":null},{"id":"2502.20003","title":"Asymptotics of Non-Convex Generalized Linear Models in High-Dimensions: A proof of the replica formula","created":"2025-02-27","cited_by_count":14,"area":null},{"id":"2402.01297","title":"Characterizing Overfitting in Kernel Ridgeless Regression Through the Eigenspectrum","created":"2024-02-02","cited_by_count":13,"area":null},{"id":"2202.02877","title":"HARFE: Hard-Ridge Random Feature Expansion","created":"2022-02-06","cited_by_count":12,"area":null},{"id":"2307.07785","title":"The Interpolating Information Criterion for Overparameterized Models","created":"2023-07-15","cited_by_count":12,"area":null},{"id":"2112.15250","title":"Benign Overfitting in Adversarially Robust Linear Classification","created":"2021-12-31","cited_by_count":11,"area":null},{"id":"2206.13482","title":"Understanding Benign Overfitting in Gradient-Based Meta Learning","created":"2022-06-27","cited_by_count":11,"area":null},{"id":"2210.07612","title":"Monotonicity and Double Descent in Uncertainty Estimation with Gaussian Processes","created":"2022-10-14","cited_by_count":11,"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":"2307.15396","title":"Noisy Interpolation Learning with Shallow Univariate ReLU Networks","created":"2023-07-28","cited_by_count":11,"area":null},{"id":"2309.04268","title":"Optimal Rate of Kernel Regression in Large Dimensions","created":"2023-09-08","cited_by_count":11,"area":null},{"id":"2401.01270","title":"Optimal Rates of Kernel Ridge Regression under Source Condition in Large Dimensions","created":"2024-01-02","cited_by_count":11,"area":null},{"id":"2104.12232","title":"Variational Inference in high-dimensional linear regression","created":"2021-04-25","cited_by_count":10,"area":null},{"id":"2106.02720","title":"An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning","created":"2021-06-04","cited_by_count":10,"area":null},{"id":"2301.07605","title":"Strong inductive biases provably prevent harmless interpolation","created":"2023-01-18","cited_by_count":10,"area":null},{"id":"2305.19377","title":"Benign Overfitting in Deep Neural Networks under Lazy Training","created":"2023-05-30","cited_by_count":10,"area":null},{"id":"2310.01975","title":"Benign Overfitting in Two-Layer ReLU Convolutional Neural Networks for XOR Data","created":"2023-10-03","cited_by_count":10,"area":null},{"id":"2206.09136","title":"Provable Generalization of Overparameterized Meta-learning Trained with SGD","created":"2022-06-18","cited_by_count":9,"area":null},{"id":"2307.14531","title":"Controlling the Inductive Bias of Wide Neural Networks by Modifying the Kernel's Spectrum","created":"2023-07-26","cited_by_count":9,"area":null},{"id":"2404.12597","title":"The phase diagram of kernel interpolation in large dimensions","created":"2024-04-19","cited_by_count":9,"area":null},{"id":"2502.13283","title":"Benefits of Early Stopping in Gradient Descent for Overparameterized Logistic Regression","created":"2025-02-18","cited_by_count":9,"area":null},{"id":"2203.03159","title":"Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime","created":"2022-03-07","cited_by_count":8,"area":null},{"id":"2206.01399","title":"Generalization for multiclass classification with overparameterized linear models","created":"2022-06-03","cited_by_count":8,"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":"2303.02255","title":"Finite-Sample Analysis of Learning High-Dimensional Single ReLU Neuron","created":"2023-03-03","cited_by_count":8,"area":null},{"id":"2310.00987","title":"A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression","created":"2023-10-02","cited_by_count":8,"area":null},{"id":"2403.07264","title":"Near-Interpolators: Rapid Norm Growth and the Trade-Off between Interpolation and Generalization","created":"2024-03-12","cited_by_count":8,"area":null},{"id":"2410.00645","title":"LoRanPAC: Low-rank Random Features and Pre-trained Models for Bridging Theory and Practice in Continual Learning","created":"2024-10-01","cited_by_count":8,"area":null},{"id":"2110.11477","title":"Conditioning of Random Feature Matrices: Double Descent and Generalization Error","created":"2021-10-21","cited_by_count":7,"area":null},{"id":"2207.11621","title":"A Universal Trade-off Between the Model Size, Test Loss, and Training Loss of Linear Predictors","created":"2022-07-23","cited_by_count":7,"area":null},{"id":"2311.14222","title":"Risk Bounds of Accelerated SGD for Overparameterized Linear Regression","created":"2023-11-23","cited_by_count":7,"area":null},{"id":"2403.17592","title":"On the Benefits of Over-parameterization for Out-of-Distribution Generalization","created":"2024-03-26","cited_by_count":7,"area":null},{"id":"2409.03891","title":"Overfitting Behaviour of Gaussian Kernel Ridgeless Regression: Varying Bandwidth or Dimensionality","created":"2024-09-05","cited_by_count":7,"area":null},{"id":"2410.17796","title":"A Comprehensive Analysis on the Learning Curve in Kernel Ridge Regression","created":"2024-10-23","cited_by_count":7,"area":null},{"id":"2310.00176","title":"Universality of max-margin classifiers","created":"2023-09-29","cited_by_count":6,"area":null},{"id":"2201.04545","title":"On generalization bounds for deep networks based on loss surface implicit regularization","created":"2022-01-12","cited_by_count":5,"area":null},{"id":"2206.00501","title":"Benign Overfitting in Classification: Provably Counter Label Noise with Larger Models","created":"2022-06-01","cited_by_count":5,"area":null},{"id":"2306.11017","title":"High-dimensional Contextual Bandit Problem without Sparsity","created":"2023-06-19","cited_by_count":5,"area":null},{"id":"2306.13185","title":"An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression","created":"2023-06-22","cited_by_count":5,"area":null},{"id":"2410.19092","title":"Provable Tempered Overfitting of Minimal Nets and Typical Nets","created":"2024-10-24","cited_by_count":5,"area":null},{"id":"2504.04039","title":"Memory-Statistics Tradeoff in Continual Learning with Structural Regularization","created":"2025-04-05","cited_by_count":5,"area":null},{"id":"2602.02285","title":"AI4SLT: Empirical Processes in Lean 4 for Formal Statistical Learning Theory","created":"2026-02-02","cited_by_count":5,"area":null},{"id":"2204.08369","title":"Benign Overfitting in Time Series Linear Models with Over-Parameterization","created":"2022-04-18","cited_by_count":4,"area":null},{"id":"2206.14255","title":"Target alignment in truncated kernel ridge regression","created":"2022-06-28","cited_by_count":4,"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":"2304.13761","title":"Enhancing Robustness of Gradient-Boosted Decision Trees through One-Hot Encoding and Regularization","created":"2023-04-26","cited_by_count":4,"area":null},{"id":"2305.06894","title":"Reinterpreting causal discovery as the task of predicting unobserved joint statistics","created":"2023-05-11","cited_by_count":4,"area":null},{"id":"2505.22048","title":"Learning Curves of Stochastic Gradient Descent in Kernel Regression","created":"2025-05-28","cited_by_count":4,"area":null},{"id":"2510.17526","title":"How Does Label Noise Gradient Descent Improve Generalization in the Low SNR Regime?","created":"2025-10-20","cited_by_count":4,"area":null},{"id":"2511.03892","title":"A general technique for approximating high-dimensional empirical kernel matrices","created":"2025-11-05","cited_by_count":4,"area":null},{"id":"2603.14578","title":"Power-Law Spectrum of the Random Feature Model","created":"2026-03-15","cited_by_count":4,"area":null},{"id":"2606.24998","title":"Internal Data Repetition Destroys Language Models","created":"2026-06-23","cited_by_count":4,"area":null},{"id":"2102.12066","title":"On the Minimal Error of Empirical Risk Minimization","created":"2021-02-24","cited_by_count":3,"area":null},{"id":"2202.06054","title":"Towards Data-Algorithm Dependent Generalization: a Case Study on Overparameterized Linear Regression","created":"2022-02-12","cited_by_count":3,"area":null},{"id":"2204.02678","title":"Random Features Model with General Convex Regularization: A Fine Grained Analysis with Precise Asymptotic Learning Curves","created":"2022-04-06","cited_by_count":3,"area":null},{"id":"2206.13347","title":"Benign overfitting and adaptive nonparametric regression","created":"2022-06-27","cited_by_count":3,"area":null},{"id":"2208.08003","title":"Investigating the Impact of Model Width and Density on Generalization in Presence of Label Noise","created":"2022-08-17","cited_by_count":3,"area":null},{"id":"2305.02304","title":"New Equivalences Between Interpolation and SVMs: Kernels and Structured Features","created":"2023-05-03","cited_by_count":3,"area":null},{"id":"2403.08585","title":"Improving Implicit Regularization of SGD with Preconditioning for Least Square Problems","created":"2024-03-13","cited_by_count":3,"area":null},{"id":"2409.12446","title":"Neural Networks Generalize on Low Complexity Data","created":"2024-09-19","cited_by_count":3,"area":null},{"id":"2501.17151","title":"Scanning Trojaned Models Using Out-of-Distribution Samples","created":"2025-01-28","cited_by_count":3,"area":"safety"},{"id":"2508.07571","title":"Towards Theoretical Understanding of Transformer Test-Time Computing: Investigation on In-Context Linear Regression","created":"2025-08-11","cited_by_count":3,"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":"2102.07181","title":"Distribution Free Uncertainty for the Minimum Norm Solution of Over-parameterized Linear Regression","created":"2021-02-14","cited_by_count":2,"area":null},{"id":"2202.09054","title":"Interpolation and Regularization for Causal Learning","created":"2022-02-18","cited_by_count":2,"area":null},{"id":"2207.13572","title":"Membership Inference Attacks via Adversarial Examples","created":"2022-07-27","cited_by_count":2,"area":"safety"},{"id":"2402.11742","title":"Balanced Data, Imbalanced Spectra: Unveiling Class Disparities with Spectral Imbalance","created":"2024-02-18","cited_by_count":2,"area":null},{"id":"2405.15115","title":"Towards Better Understanding of In-Context Learning Ability from In-Context Uncertainty Quantification","created":"2024-05-24","cited_by_count":2,"area":null},{"id":"2406.01435","title":"Learning Analysis of Kernel Ridgeless Regression with Asymmetric Kernel Learning","created":"2024-06-03","cited_by_count":2,"area":null},{"id":"2502.07480","title":"Beyond Benign Overfitting in Nadaraya-Watson Interpolators","created":"2025-02-11","cited_by_count":2,"area":null},{"id":"2502.12508","title":"Understanding Generalization in Transformers: Error Bounds and Training Dynamics Under Benign and Harmful Overfitting","created":"2025-02-18","cited_by_count":2,"area":null}],"next_cursor":"100"}