statistical learning theory

On the Saturation Effect of Kernel Ridge Regression ICLR 2023 On the Variance, Admissibility, and Stability of Empirical Risk Minimization NeurIPS 2023 Optimal Convergence Rates for Distributed Nystroem Approximation JMLR 2023 Revisiting minimum description length complexity in overparameterized models JMLR 2023 Sampling-based Nyström Approximation and Kernel Quadrature ICML 2023 Unified Lower Bounds for Interactive High-dimensional Estimation under Information Constraints NeurIPS 2023 KAM Theory Meets Statistical Learning Theory: Hamiltonian Neural Networks with Non-zero Training Loss AAAI 2022 Model Zoo: A Growing Brain That Learns Continually ICLR 2022 Online PAC-Bayes Learning NeurIPS 2022 On the Statistical Benefits of Curriculum Learning ICML 2022 Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression NeurIPS 2022 Rate-Distortion Theoretic Generalization Bounds for Stochastic Learning Algorithms COLT 2022 Statistical Rates of Convergence for Functional Partially Linear Support Vector Machines for Classification JMLR 2022 Strong Gaussian Approximation for the Sum of Random Vectors COLT 2022 Adaptive estimation of nonparametric functionals JMLR 2021 A Precise Performance Analysis of Support Vector Regression ICML 2021 Differentially Private Assouad, Fano, and Le Cam ALT 2021 Failures of Model-dependent Generalization Bounds for Least-norm Interpolation JMLR 2021 Good Classifiers are Abundant in the Interpolating Regime AISTATS 2021 On Optimal Interpolation in Linear Regression NeurIPS 2021 Predictive Power of Nearest Neighbors Algorithm under Random Perturbation AISTATS 2021 Risk Bounds and Rademacher Complexity in Batch Reinforcement Learning ICML 2021 Estimating weighted areas under the ROC curve NeurIPS 2020 Minimax Estimation of Conditional Moment Models NeurIPS 2020 Near-Tight Margin-Based Generalization Bounds for Support Vector Machines ICML 2020