{"total":63,"rows":[{"id":"2103.01550","title":"Label-Imbalanced and Group-Sensitive Classification under Overparameterization","created":"2021-03-02","cited_by_count":74,"area":null},{"id":"2102.11742","title":"Classifying high-dimensional Gaussian mixtures: Where kernel methods fail and neural networks succeed","created":"2021-02-23","cited_by_count":69,"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":"2106.03791","title":"Learning Gaussian Mixtures with Generalised Linear Models: Precise Asymptotics in High-dimensions","created":"2021-06-07","cited_by_count":54,"area":null},{"id":"2211.11567","title":"Neural networks trained with SGD learn distributions of increasing complexity","created":"2022-11-21","cited_by_count":50,"area":null},{"id":"2402.04980","title":"Asymptotics of feature learning in two-layer networks after one gradient-step","created":"2024-02-07","cited_by_count":38,"area":null},{"id":"2402.03902","title":"A phase transition between positional and semantic learning in a solvable model of dot-product attention","created":"2024-02-06","cited_by_count":30,"area":null},{"id":"2302.08933","title":"Universality laws for Gaussian mixtures in generalized linear models","created":"2023-02-17","cited_by_count":28,"area":null},{"id":"2205.13303","title":"Gaussian Universality of Perceptrons with Random Labels","created":"2022-05-26","cited_by_count":26,"area":null},{"id":"2302.08923","title":"Are Gaussian data all you need? Extents and limits of universality in high-dimensional generalized linear estimation","created":"2023-02-17","cited_by_count":26,"area":null},{"id":"2101.01918","title":"Phase Transitions in Transfer Learning for High-Dimensional Perceptrons","created":"2021-01-06","cited_by_count":22,"area":null},{"id":"2102.07379","title":"On the Inherent Regularization Effects of Noise Injection During Training","created":"2021-02-15","cited_by_count":18,"area":null},{"id":"2202.03295","title":"Theoretical characterization of uncertainty in high-dimensional linear classification","created":"2022-02-07","cited_by_count":18,"area":null},{"id":"2203.12094","title":"Learning curves for the multi-class teacher-student perceptron","created":"2022-03-22","cited_by_count":17,"area":"distill"},{"id":"2402.18551","title":"Implicit Optimization Bias of Next-Token Prediction in Linear Models","created":"2024-02-28","cited_by_count":14,"area":null},{"id":"2103.05524","title":"On the interplay between data structure and loss function in classification problems","created":"2021-03-09","cited_by_count":13,"area":null},{"id":"2212.13069","title":"Homophily modulates double descent generalization in graph convolution networks","created":"2022-12-26","cited_by_count":13,"area":null},{"id":"2204.02704","title":"Fundamental limits to learning closed-form mathematical models from data","created":"2022-04-06","cited_by_count":12,"area":null},{"id":"2409.13904","title":"High-dimensional learning of narrow neural networks","created":"2024-09-20","cited_by_count":12,"area":null},{"id":"2110.00926","title":"Information-Theoretic Characterization of the Generalization Error for Iterative Semi-Supervised Learning","created":"2021-10-03","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":"2304.02912","title":"Classification of Heavy-tailed Features in High Dimensions: a Superstatistical Approach","created":"2023-04-06","cited_by_count":11,"area":null},{"id":"2205.15935","title":"Bias-inducing geometries: an exactly solvable data model with fairness implications","created":"2022-05-31","cited_by_count":9,"area":null},{"id":"2311.02270","title":"Regularized Linear Regression for Binary Classification","created":"2023-11-03","cited_by_count":7,"area":null},{"id":"2402.05674","title":"A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs","created":"2024-02-08","cited_by_count":7,"area":null},{"id":"2103.05621","title":"The Common Intuition to Transfer Learning Can Win or Lose: Case Studies for Linear Regression","created":"2021-03-09","cited_by_count":6,"area":null},{"id":"2405.09535","title":"Restoring balance: principled under/oversampling of data for optimal classification","created":"2024-05-15","cited_by_count":6,"area":null},{"id":"2103.15996","title":"Minimum complexity interpolation in random features models","created":"2021-03-30","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":"2309.07663","title":"High-dimensional Asymptotics of VAEs: Threshold of Posterior Collapse and Dataset-Size Dependence of Rate-Distortion Curve","created":"2023-09-14","cited_by_count":5,"area":null},{"id":"2303.02048","title":"Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture","created":"2023-03-03","cited_by_count":4,"area":null},{"id":"2305.14689","title":"Least Squares Regression Can Exhibit Under-Parameterized Double Descent","created":"2023-05-24","cited_by_count":4,"area":null},{"id":"2402.07356","title":"A Novel Gaussian Min-Max Theorem and its Applications","created":"2024-02-12","cited_by_count":4,"area":null},{"id":"2404.09779","title":"A replica analysis of under-bagging","created":"2024-04-15","cited_by_count":4,"area":null},{"id":"2410.05609","title":"The Breakdown of Gaussian Universality in Classification of High-dimensional Linear Factor Mixtures","created":"2024-10-08","cited_by_count":4,"area":null},{"id":"2410.16073","title":"On the Geometry of Regularization in Adversarial Training: High-Dimensional Asymptotics and Generalization Bounds","created":"2024-10-21","cited_by_count":4,"area":null},{"id":"2503.00856","title":"Asymptotic Analysis of Two-Layer Neural Networks after One Gradient Step under Gaussian Mixtures Data with Structure","created":"2025-03-02","cited_by_count":4,"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":"2402.03818","title":"Asymptotic generalization error of a single-layer graph convolutional network","created":"2024-02-06","cited_by_count":3,"area":null},{"id":"2507.07907","title":"A statistical physics framework for optimal learning","created":"2025-07-10","cited_by_count":3,"area":null},{"id":"2306.06649","title":"Efficient Learning of Minimax Risk Classifiers in High Dimensions","created":"2023-06-11","cited_by_count":2,"area":null},{"id":"2501.16226","title":"The Effect of Optimal Self-Distillation in Noisy Gaussian Mixture Model","created":"2025-01-27","cited_by_count":2,"area":"distill"},{"id":"2503.01361","title":"Statistical physics analysis of graph neural networks: Approaching optimality in the contextual stochastic block model","created":"2025-03-03","cited_by_count":2,"area":null},{"id":"2506.12454","title":"On the Existence of Consistent Adversarial Attacks in High-Dimensional Linear Classification","created":"2025-06-14","cited_by_count":2,"area":null},{"id":"2507.00629","title":"Generalization performance of narrow one-hidden layer networks in the teacher-student setting","created":"2025-07-01","cited_by_count":2,"area":"distill"},{"id":"2112.01292","title":"Optimal regularizations for data generation with probabilistic graphical models","created":"2021-12-02","cited_by_count":1,"area":null},{"id":"2402.10474","title":"One-Bit Quantization and Sparsification for Multiclass Linear Classification with Strong Regularization","created":"2024-02-16","cited_by_count":1,"area":null},{"id":"2409.05598","title":"When resampling/reweighting improves feature learning in imbalanced classification?: A toy-model study","created":"2024-09-09","cited_by_count":1,"area":null},{"id":"2501.11638","title":"Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model","created":"2025-01-20","cited_by_count":1,"area":null},{"id":"2506.20025","title":"Thumb on the Scale: Optimal Loss Weighting in Last Layer Retraining","created":"2025-06-24","cited_by_count":1,"area":null},{"id":"2509.21174","title":"Breaking the curse of dimensionality for linear rules: optimal predictors over the ellipsoid","created":"2025-09-25","cited_by_count":1,"area":null},{"id":"2602.12039","title":"The Implicit Bias of Logit Regularization","created":"2026-02-12","cited_by_count":1,"area":null},{"id":"2605.14927","title":"Learning with Shallow Neural Networks on Cluster-Structured Features","created":"2026-05-14","cited_by_count":1,"area":null},{"id":"2201.09986","title":"Bayesian Inference with Nonlinear Generative Models: Comments on Secure Learning","created":"2022-01-19","cited_by_count":0,"area":null},{"id":"2401.12610","title":"The twin peaks of learning neural networks","created":"2024-01-23","cited_by_count":0,"area":null},{"id":"2405.18427","title":"Classifying Overlapping Gaussian Mixtures in High Dimensions: From Optimal Classifiers to Neural Nets","created":"2024-05-28","cited_by_count":0,"area":null},{"id":"2410.19950","title":"Statistical Inference in Classification of High-dimensional Gaussian Mixture","created":"2024-10-25","cited_by_count":0,"area":null},{"id":"2411.19553","title":"Analysis of High-dimensional Gaussian Labeled-unlabeled Mixture Model via Message-passing Algorithm","created":"2024-11-29","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},{"id":"2505.08129","title":"High-order Regularization for Machine Learning and Learning-based Control","created":"2025-05-13","cited_by_count":0,"area":"deeprl"},{"id":"2511.17626","title":"Efficient Large-Scale Learning of Minimax Risk Classifiers","created":"2025-11-18","cited_by_count":0,"area":null},{"id":"2602.00827","title":"Over-Alignment vs Over-Fitting: The Role of Feature Learning Strength in Generalization","created":"2026-01-31","cited_by_count":0,"area":null},{"id":"2609.36117","title":"Why Backdooring Neural Networks is so Easy?","created":"2026-09-28","cited_by_count":0,"area":null}],"next_cursor":null}