double descent

Risk Monotonicity in Statistical Learning NeurIPS 2021 Taxonomizing local versus global structure in neural network loss landscapes NeurIPS 2021 The curious case of adversarially robust models: More data can help, double descend, or hurt generalization UAI 2021 A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent NeurIPS 2020 Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model NeurIPS 2020 Deep Double Descent: Where Bigger Models and More Data Hurt ICLR 2020 Double Trouble in Double Descent: Bias and Variance(s) in the Lazy Regime ICML 2020 Do We Need Zero Training Loss After Achieving Zero Training Error? ICML 2020 Exact expressions for double descent and implicit regularization via surrogate random design NeurIPS 2020 Finite Versus Infinite Neural Networks: an Empirical Study NeurIPS 2020 Generalisation error in learning with random features and the hidden manifold model ICML 2020 Generalization Error of Generalized Linear Models in High Dimensions ICML 2020 Generalization of Two-layer Neural Networks: An Asymptotic Viewpoint ICLR 2020 On the Optimal Weighted ℓ_2 Regularization in Overparameterized Linear Regression NeurIPS 2020 Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree NeurIPS 2020 Subspace Fitting Meets Regression: The Effects of Supervision and Orthonormality Constraints on Double Descent of Generalization Errors ICML 2020 The Neural Tangent Kernel in High Dimensions: Triple Descent and a Multi-Scale Theory of Generalization ICML 2020 Triple descent and the two kinds of overfitting: where & why do they appear? NeurIPS 2020 Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition NeurIPS 2020 On the number of variables to use in principal component regression NeurIPS 2019