test error

Are all models wrong? Fundamental limits in distribution-free empirical model falsification COLT 2025 An Agnostic View on the Cost of Overfitting in (Kernel) Ridge Regression ICLR 2024 Asymptotics of Learning with Deep Structured (Random) Features ICML 2024 Bayes-optimal learning of an extensive-width neural network from quadratically many samples NeurIPS 2024 Scaling Laws in Linear Regression: Compute, Parameters, and Data NeurIPS 2024 (Almost) Provable Error Bounds Under Distribution Shift via Disagreement Discrepancy NeurIPS 2023 A Theoretical Analysis of the Test Error of Finite-Rank Kernel Ridge Regression NeurIPS 2023 Deterministic equivalent and error universality of deep random features learning ICML 2023 Optimal Activation Functions for the Random Features Regression Model ICLR 2023 Assessing Generalization of SGD via Disagreement ICLR 2022 Universality of empirical risk minimization COLT 2022 Asymptotics of Ridge(less) Regression under General Source Condition AISTATS 2021 Learning Curves for Analysis of Deep Networks ICML 2021 Relative stability toward diffeomorphisms indicates performance in deep nets NeurIPS 2021 Rethink the Connections among Generalization, Memorization, and the Spectral Bias of DNNs IJCAI 2021 Cross-validation Confidence Intervals for Test Error NeurIPS 2020 Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks ICLR 2020 Adversarial Examples Are a Natural Consequence of Test Error in Noise ICML 2019 Weighted Sums of Random Kitchen Sinks: Replacing minimization with randomization in learning NeurIPS 2008