{"total":75,"rows":[{"id":"2102.06701","title":"Explaining Neural Scaling Laws","created":"2021-02-12","cited_by_count":363,"area":null},{"id":"2402.01092","title":"A Dynamical Model of Neural Scaling Laws","created":"2024-02-02","cited_by_count":111,"area":null},{"id":"2202.06985","title":"Deep Ensembles Work, But Are They Necessary?","created":"2022-02-14","cited_by_count":63,"area":null},{"id":"2203.08124","title":"Can Neural Nets Learn the Same Model Twice? Investigating Reproducibility and Double Descent from the Decision Boundary Perspective","created":"2022-03-15","cited_by_count":57,"area":null},{"id":"2405.00592","title":"Scaling and renormalization in high-dimensional regression","created":"2024-05-01","cited_by_count":50,"area":null},{"id":"2310.07891","title":"A Theory of Non-Linear Feature Learning with One Gradient Step in Two-Layer Neural Networks","created":"2023-10-11","cited_by_count":49,"area":null},{"id":"2305.18411","title":"Feature-Learning Networks Are Consistent Across Widths At Realistic Scales","created":"2023-05-28","cited_by_count":41,"area":null},{"id":"2303.14151","title":"Double Descent Demystified: Identifying, Interpreting & Ablating the Sources of a Deep Learning Puzzle","created":"2023-03-24","cited_by_count":37,"area":null},{"id":"2302.00401","title":"Deterministic equivalent and error universality of deep random features learning","created":"2023-02-01","cited_by_count":35,"area":null},{"id":"2212.12147","title":"The Onset of Variance-Limited Behavior for Networks in the Lazy and Rich Regimes","created":"2022-12-23","cited_by_count":25,"area":null},{"id":"2203.00573","title":"Contrasting random and learned features in deep Bayesian linear regression","created":"2022-03-01","cited_by_count":24,"area":null},{"id":"2206.07252","title":"Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions","created":"2022-06-15","cited_by_count":23,"area":null},{"id":"2205.14846","title":"Precise Learning Curves and Higher-Order Scaling Limits for Dot Product Kernel Regression","created":"2022-05-30","cited_by_count":22,"area":null},{"id":"2210.12256","title":"Uncertainty Estimates of Predictions via a General Bias-Variance Decomposition","created":"2022-10-21","cited_by_count":20,"area":null},{"id":"2201.13383","title":"Fluctuations, Bias, Variance & Ensemble of Learners: Exact Asymptotics for Convex Losses in High-Dimension","created":"2022-01-31","cited_by_count":19,"area":null},{"id":"2502.02531","title":"Deep Linear Network Training Dynamics from Random Initialization: Data, Width, Depth, and Hyperparameter Transfer","created":"2025-02-04","cited_by_count":19,"area":null},{"id":"2302.00704","title":"Pathologies of Predictive Diversity in Deep Ensembles","created":"2023-02-01","cited_by_count":16,"area":null},{"id":"2312.10935","title":"AEDFL: Efficient Asynchronous Decentralized Federated Learning with Heterogeneous Devices","created":"2023-12-18","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":"2103.04554","title":"Exact Gap between Generalization Error and Uniform Convergence in Random Feature Models","created":"2021-03-08","cited_by_count":13,"area":null},{"id":"2303.00564","title":"Learning curves for deep structured Gaussian feature models","created":"2023-03-01","cited_by_count":13,"area":null},{"id":"2301.13371","title":"Demystifying Disagreement-on-the-Line in High Dimensions","created":"2023-01-31","cited_by_count":12,"area":null},{"id":"2110.03789","title":"Knowledge Sheaves: A Sheaf-Theoretic Framework for Knowledge Graph Embedding","created":"2021-10-07","cited_by_count":11,"area":null},{"id":"2202.04167","title":"Understanding the bias-variance tradeoff of Bregman divergences","created":"2022-02-08","cited_by_count":11,"area":null},{"id":"2304.13016","title":"Subsample Ridge Ensembles: Equivalences and Generalized Cross-Validation","created":"2023-04-25","cited_by_count":11,"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":"2203.03443","title":"Generalization Through The Lens Of Leave-One-Out Error","created":"2022-03-07","cited_by_count":10,"area":null},{"id":"2208.09897","title":"Multiple Descent in the Multiple Random Feature Model","created":"2022-08-21","cited_by_count":10,"area":null},{"id":"2305.18496","title":"Generalized equivalences between subsampling and ridge regularization","created":"2023-05-29","cited_by_count":10,"area":null},{"id":"2106.03485","title":"Redundant representations help generalization in wide neural networks","created":"2021-06-07","cited_by_count":9,"area":null},{"id":"2206.01378","title":"Regularization-wise double descent: Why it occurs and how to eliminate it","created":"2022-06-03","cited_by_count":9,"area":null},{"id":"2411.00247","title":"Deep Learning Through A Telescoping Lens: A Simple Model Provides Empirical Insights On Grokking, Gradient Boosting & Beyond","created":"2024-10-31","cited_by_count":9,"area":null},{"id":"2501.10077","title":"Double descent in quantum kernel methods","created":"2025-01-17","cited_by_count":9,"area":null},{"id":"2406.09366","title":"Towards an Improved Understanding and Utilization of Maximum Manifold Capacity Representations","created":"2024-06-13","cited_by_count":8,"area":null},{"id":"2408.04607","title":"Risk and cross validation in ridge regression with correlated samples","created":"2024-08-08","cited_by_count":8,"area":null},{"id":"2107.12685","title":"On the Role of Optimization in Double Descent: A Least Squares Study","created":"2021-07-27","cited_by_count":7,"area":null},{"id":"2203.05443","title":"Bias-variance decomposition of overparameterized regression with random linear features","created":"2022-03-10","cited_by_count":7,"area":null},{"id":"2206.10566","title":"Ensembling over Classifiers: a Bias-Variance Perspective","created":"2022-06-21","cited_by_count":6,"area":null},{"id":"2306.05325","title":"Federated Learning under Covariate Shifts with Generalization Guarantees","created":"2023-06-08","cited_by_count":6,"area":null},{"id":"2410.16201","title":"Theoretical Limitations of Ensembles in the Age of Overparameterization","created":"2024-10-21","cited_by_count":6,"area":null},{"id":"2102.07238","title":"Double-descent curves in neural networks: a new perspective using Gaussian processes","created":"2021-02-14","cited_by_count":5,"area":null},{"id":"2106.06153","title":"Towards Understanding Generalization via Decomposing Excess Risk Dynamics","created":"2021-06-11","cited_by_count":5,"area":null},{"id":"2110.06910","title":"On the Double Descent of Random Features Models Trained with SGD","created":"2021-10-13","cited_by_count":5,"area":null},{"id":"2212.06757","title":"Gradient flow in the gaussian covariate model: exact solution of learning curves and multiple descent structures","created":"2022-12-13","cited_by_count":5,"area":null},{"id":"2310.01374","title":"Corrected generalized cross-validation for finite ensembles of penalized estimators","created":"2023-10-02","cited_by_count":5,"area":null},{"id":"2409.18842","title":"Classical Statistical (In-Sample) Intuitions Don't Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs","created":"2024-09-27","cited_by_count":5,"area":null},{"id":"2103.14108","title":"The Geometry of Over-parameterized Regression and Adversarial Perturbations","created":"2021-03-25","cited_by_count":4,"area":null},{"id":"2402.13622","title":"Analysis of Bootstrap and Subsampling in High-dimensional Regularized Regression","created":"2024-02-21","cited_by_count":4,"area":null},{"id":"2402.16793","title":"Failures and Successes of Cross-Validation for Early-Stopped Gradient Descent","created":"2024-02-26","cited_by_count":4,"area":null},{"id":"2407.20724","title":"Exploring Loss Landscapes through the Lens of Spin Glass Theory","created":"2024-07-30","cited_by_count":4,"area":null},{"id":"2409.05782","title":"Unified Neural Network Scaling Laws and Scale-time Equivalence","created":"2024-09-09","cited_by_count":4,"area":null},{"id":"2502.10442","title":"Analysis of Overparameterization in Continual Learning under a Linear Model","created":"2025-02-11","cited_by_count":4,"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":"2304.10510","title":"Censoring chemical data to mitigate dual use risk","created":"2023-04-20","cited_by_count":3,"area":null},{"id":"2407.09845","title":"Towards Understanding Epoch-wise Double descent in Two-layer Linear Neural Networks","created":"2024-07-13","cited_by_count":3,"area":null},{"id":"2502.18339","title":"Correlating and Predicting Human Evaluations of Language Models from Natural Language Processing Benchmarks","created":"2025-02-24","cited_by_count":3,"area":null},{"id":"2506.23286","title":"Not All Explanations for Deep Learning Phenomena Are Equally Valuable","created":"2025-06-29","cited_by_count":3,"area":null},{"id":"2204.12155","title":"Bias-Variance Decompositions for Margin Losses","created":"2022-04-26","cited_by_count":2,"area":null},{"id":"2302.05807","title":"Pushing the Accuracy-Group Robustness Frontier with Introspective Self-play","created":"2023-02-11","cited_by_count":2,"area":null},{"id":"2404.12481","title":"Understanding Optimal Feature Transfer via a Fine-Grained Bias-Variance Analysis","created":"2024-04-18","cited_by_count":2,"area":null},{"id":"2601.04301","title":"Quantifying the Effect of Test Set Contamination on Generative Evaluations","created":"2026-01-07","cited_by_count":2,"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":"2310.09250","title":"It's an Alignment, Not a Trade-off: Revisiting Bias and Variance in Deep Models","created":"2023-10-13","cited_by_count":1,"area":null},{"id":"2310.13836","title":"Foundation Model's Embedded Representations May Detect Distribution Shift","created":"2023-10-20","cited_by_count":1,"area":null},{"id":"2412.05418","title":"No Free Lunch From Random Feature Ensembles: Scaling Laws and Near-Optimality Conditions","created":"2024-12-06","cited_by_count":1,"area":null},{"id":"2509.21181","title":"Closed-form $\\ell_r$ norm scaling with data for overparameterized linear regression and diagonal linear networks under $\\ell_p$ bias","created":"2025-09-25","cited_by_count":1,"area":null},{"id":"2306.05583","title":"Gibbs-Based Information Criteria and the Over-Parameterized Regime","created":"2023-06-08","cited_by_count":0,"area":null},{"id":"2310.10791","title":"Neural Tangent Kernels Motivate Graph Neural Networks with Cross-Covariance Graphs","created":"2023-10-16","cited_by_count":0,"area":null},{"id":"2408.08294","title":"eGAD! double descent is explained by Generalized Aliasing Decomposition","created":"2024-08-15","cited_by_count":0,"area":null},{"id":"2504.10754","title":"auto-fpt: Automating Free Probability Theory Calculations for Machine Learning Theory","created":"2025-04-14","cited_by_count":0,"area":null},{"id":"2512.11089","title":"TPV: Parameter Perturbations Through the Lens of Test Prediction Variance","created":"2025-12-11","cited_by_count":0,"area":null},{"id":"2601.08316","title":"Deep Exploration of Epoch-wise Double Descent in Noisy Data: Signal Separation, Large Activation, and Benign Overfitting","created":"2026-01-13","cited_by_count":0,"area":null},{"id":"2605.17767","title":"Feature Learning in Linear-Width Two-Layer Networks: Two vs. One Step of Gradient Descent","created":"2026-05-18","cited_by_count":0,"area":null},{"id":"2606.23477","title":"Sublinearly Structured Deep Neural Networks Achieve Feature Learning Consistency for Compositional Functions","created":"2026-06-22","cited_by_count":0,"area":null},{"id":"2610.13524","title":"Simulating Training Spikes of Deep Neural Networks by Layer-wise Random Feature Models","created":"2026-10-09","cited_by_count":0,"area":null}],"next_cursor":null}