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Regression

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Papers

Semi-parametric Learning of Structured Temporal Point Processes JMLR 2020
Conic Optimization for Quadratic Regression Under Sparse Noise JMLR 2020
Empirical Risk Minimization in the Non-interactive Local Model of Differential Privacy JMLR 2020
Continuous-Time Birth-Death MCMC for Bayesian Regression Tree Models JMLR 2020
Two-Stage Approach to Multivariate Linear Regression with Sparsely Mismatched Data JMLR 2020
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms JMLR 2020
Functional Martingale Residual Process for High-Dimensional Cox Regression with Model Averaging JMLR 2020
Ultra-High Dimensional Single-Index Quantile Regression JMLR 2020
A Sparse Semismooth Newton Based Proximal Majorization-Minimization Algorithm for Nonconvex Square-Root-Loss Regression Problems JMLR 2020
Significance Tests for Neural Networks JMLR 2020
Stable Regression: On the Power of Optimization over Randomization JMLR 2020
Spectral Deconfounding via Perturbed Sparse Linear Models JMLR 2020
Robust high dimensional learning for Lipschitz and convex losses JMLR 2020
Dual Extrapolation for Sparse GLMs JMLR 2020
Minimal Learning Machine: Theoretical Results and Clustering-Based Reference Point Selection JMLR 2020
Rank-based Lasso - efficient methods for high-dimensional robust model selection JMLR 2020
High-dimensional quantile tensor regression JMLR 2020
Actively Learning Gaussian Process Dynamics L4DC 2020
Finite Sample System Identification: Optimal Rates and the Role of Regularization L4DC 2020
Linear Antisymmetric Recurrent Neural Networks L4DC 2020
Black-box continuous-time transfer function estimation with stability guarantees: a kernel-based approach L4DC 2020
Robust Regression for Safe Exploration in Control L4DC 2020
A First Principles Approach for Data-Efficient System Identification of Spring-Rod Systems via Differentiable Physics Engines L4DC 2020
Model-Based Reinforcement Learning with Value-Targeted Regression L4DC 2020
Learning Dynamical Systems with Side Information L4DC 2020