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Regression

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Papers

Global and Quadratic Convergence of Newton Hard-Thresholding Pursuit JMLR 2021
Homogeneity Structure Learning in Large-scale Panel Data with Heavy-tailed Errors JMLR 2021
Entangled Kernels - Beyond Separability JMLR 2021
Finite Time LTI System Identification JMLR 2021
Inference In High-dimensional Single-Index Models Under Symmetric Designs JMLR 2021
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories JMLR 2021
From Fourier to Koopman: Spectral Methods for Long-term Time Series Prediction JMLR 2021
Optimal Feedback Law Recovery by Gradient-Augmented Sparse Polynomial Regression JMLR 2021
Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach JMLR 2021
Sparse Tensor Additive Regression JMLR 2021
Testing Conditional Independence via Quantile Regression Based Partial Copulas JMLR 2021
Analyzing the discrepancy principle for kernelized spectral filter learning algorithms JMLR 2021
Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage JMLR 2021
Histogram Transform Ensembles for Large-scale Regression JMLR 2021
Learning a High-dimensional Linear Structural Equation Model via l1-Regularized Regression JMLR 2021
Non-parametric Quantile Regression via the K-NN Fused Lasso JMLR 2021
An algorithmic view of L2 regularization and some path-following algorithms JMLR 2021
Generalization Properties of hyper-RKHS and its Applications JMLR 2021
Optimal Minimax Variable Selection for Large-Scale Matrix Linear Regression Model JMLR 2021
Accelerating Ill-Conditioned Low-Rank Matrix Estimation via Scaled Gradient Descent JMLR 2021
Factorization Machines with Regularization for Sparse Feature Interactions JMLR 2021
The Ridgelet Prior: A Covariance Function Approach to Prior Specification for Bayesian Neural Networks JMLR 2021
Information criteria for non-normalized models JMLR 2021
Optimal Rates of Distributed Regression with Imperfect Kernels JMLR 2021
Unlinked Monotone Regression JMLR 2021