{"total":33,"rows":[{"id":"2208.01066","title":"What Can Transformers Learn In-Context? A Case Study of Simple Function Classes","created":"2022-08-01","cited_by_count":715,"area":null},{"id":"2102.06701","title":"Explaining Neural Scaling Laws","created":"2021-02-12","cited_by_count":363,"area":null},{"id":"2103.10948","title":"The Shape of Learning Curves: a Review","created":"2021-03-19","cited_by_count":70,"area":null},{"id":"2405.00592","title":"Scaling and renormalization in high-dimensional regression","created":"2024-05-01","cited_by_count":50,"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":"2410.09695","title":"Can In-context Learning Really Generalize to Out-of-distribution Tasks?","created":"2024-10-13","cited_by_count":23,"area":null},{"id":"2106.02261","title":"Out-of-Distribution Generalization in Kernel Regression","created":"2021-06-04","cited_by_count":21,"area":null},{"id":"2108.12006","title":"When and how epochwise double descent happens","created":"2021-08-26","cited_by_count":21,"area":null},{"id":"2203.07337","title":"Phenomenology of Double Descent in Finite-Width Neural Networks","created":"2022-03-14","cited_by_count":12,"area":null},{"id":"2102.00940","title":"Meta-learning with negative learning rates","created":"2021-02-01","cited_by_count":11,"area":null},{"id":"2202.11199","title":"Differentially Private Regression with Unbounded Covariates","created":"2022-02-19","cited_by_count":10,"area":null},{"id":"2205.12937","title":"Mitigating multiple descents: A model-agnostic framework for risk monotonization","created":"2022-05-25","cited_by_count":10,"area":null},{"id":"2605.29548","title":"Why Larger Models Learn More: Effects of Capacity, Interference, and Rare-Task Retention","created":"2026-05-28","cited_by_count":10,"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":"2206.01399","title":"Generalization for multiclass classification with overparameterized linear models","created":"2022-06-03","cited_by_count":8,"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":"2407.08641","title":"How more data can hurt: Instability and regularization in next-generation reservoir computing","created":"2024-07-11","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":"2107.00797","title":"Mitigating deep double descent by concatenating inputs","created":"2021-07-02","cited_by_count":3,"area":null},{"id":"2208.04372","title":"Generalization and Overfitting in Matrix Product State Machine Learning Architectures","created":"2022-08-08","cited_by_count":3,"area":null},{"id":"2307.07872","title":"Does Double Descent Occur in Self-Supervised Learning?","created":"2023-07-15","cited_by_count":3,"area":null},{"id":"2507.09440","title":"Transformers Don't In-Context Learn Least Squares Regression","created":"2025-07-13","cited_by_count":3,"area":null},{"id":"2211.14061","title":"A Survey of Learning Curves with Bad Behavior: or How More Data Need Not Lead to Better Performance","created":"2022-11-25","cited_by_count":2,"area":null},{"id":"2305.12219","title":"Collaborative Development of NLP models","created":"2023-05-20","cited_by_count":2,"area":null},{"id":"2109.13215","title":"Classification and Adversarial examples in an Overparameterized Linear Model: A Signal Processing Perspective","created":"2021-09-27","cited_by_count":1,"area":null},{"id":"2208.13080","title":"Information FOMO: The unhealthy fear of missing out on information. A method for removing misleading data for healthier models","created":"2022-08-27","cited_by_count":1,"area":null},{"id":"2307.03176","title":"Learning Curves for Noisy Heterogeneous Feature-Subsampled Ridge Ensembles","created":"2023-07-06","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":"2111.10754","title":"Local Linearity and Double Descent in Catastrophic Overfitting","created":"2021-11-21","cited_by_count":0,"area":null},{"id":"2207.01548","title":"Counterbalancing Teacher: Regularizing Batch Normalized Models for Robustness","created":"2022-07-04","cited_by_count":0,"area":null},{"id":"2212.00700","title":"High Dimensional Binary Classification under Label Shift: Phase Transition and Regularization","created":"2022-12-01","cited_by_count":0,"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":"2504.19351","title":"The Double Descent Behavior in Two Layer Neural Network for Binary Classification","created":"2025-04-27","cited_by_count":0,"area":null}],"next_cursor":null}