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Theory

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Papers

Doubly Robust Stein-Kernelized Monte Carlo Estimator: Simultaneous Bias-Variance Reduction and Supercanonical Convergence JMLR 2023
Concentration analysis of multivariate elliptic diffusions JMLR 2023
Dimensionless machine learning: Imposing exact units equivariance JMLR 2023
The Implicit Bias of Benign Overfitting JMLR 2023
Deep linear networks can benignly overfit when shallow ones do JMLR 2023
Maximum likelihood estimation in Gaussian process regression is ill-posed JMLR 2023
Minimal Width for Universal Property of Deep RNN JMLR 2023
Statistical Robustness of Empirical Risks in Machine Learning JMLR 2023
Generalization error bounds for multiclass sparse linear classifiers JMLR 2023
Selective inference for k-means clustering JMLR 2023
Preconditioned Gradient Descent for Overparameterized Nonconvex Burer--Monteiro Factorization with Global Optimality Certification JMLR 2023
Robust Methods for High-Dimensional Linear Learning JMLR 2023
Comprehensive Algorithm Portfolio Evaluation using Item Response Theory JMLR 2023
Quasi-Equivalence between Width and Depth of Neural Networks JMLR 2023
Metrizing Weak Convergence with Maximum Mean Discrepancies JMLR 2023
MMD Aggregated Two-Sample Test JMLR 2023
CodaLab Competitions: An Open Source Platform to Organize Scientific Challenges JMLR 2023
GANs as Gradient Flows that Converge JMLR 2023
On the Optimality of Nuclear-norm-based Matrix Completion for Problems with Smooth Non-linear Structure JMLR 2023
Revisiting minimum description length complexity in overparameterized models JMLR 2023
Deep Neural Networks with Dependent Weights: Gaussian Process Mixture Limit, Heavy Tails, Sparsity and Compressibility JMLR 2023
Erratum: Risk Bounds for the Majority Vote: From a PAC-Bayesian Analysis to a Learning Algorithm JMLR 2023
Continuous-in-time Limit for Bayesian Bandits JMLR 2023
Operator learning with PCA-Net: upper and lower complexity bounds JMLR 2023
Large data limit of the MBO scheme for data clustering: convergence of the dynamics JMLR 2023