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Regression

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Papers

Wasserstein regularization for sparse multi-task regression AISTATS 2019
Iterative Bayesian Learning for Crowdsourced Regression AISTATS 2019
Does data interpolation contradict statistical optimality? AISTATS 2019
Variable selection for Gaussian processes via sensitivity analysis of the posterior predictive distribution AISTATS 2019
The Gaussian Process Autoregressive Regression Model (GPAR) AISTATS 2019
Probabilistic Forecasting with Spline Quantile Function RNNs AISTATS 2019
Designing Optimal Binary Rating Systems AISTATS 2019
Regularized Contextual Bandits AISTATS 2019
Know Your Boundaries: Constraining Gaussian Processes by Variational Harmonic Features AISTATS 2019
Least Squares Estimation of Weakly Convex Functions AISTATS 2019
Fast Algorithms for Sparse Reduced-Rank Regression AISTATS 2019
An Online Algorithm for Smoothed Regression and LQR Control AISTATS 2019
Reducing training time by efficient localized kernel regression AISTATS 2019
Scalable High-Order Gaussian Process Regression AISTATS 2019
Multi-Observation Regression AISTATS 2019
High Dimensional Inference in Partially Linear Models AISTATS 2019
Binary Space Partitioning Forest AISTATS 2019
Lifting high-dimensional non-linear models with Gaussian regressors AISTATS 2019
Attribute-efficient learning of monomials over highly-correlated variables ALT 2019
Uniform regret bounds over $\mathbb{R}^d$ for the sequential linear regression problem with the square loss ALT 2019
Sample Compression for Real-Valued Learners ALT 2019
Noninteractive Locally Private Learning of Linear Models via Polynomial Approximations ALT 2019
The Optimal Approximation Factor in Density Estimation COLT 2019
Active Regression via Linear-Sample Sparsification COLT 2019
Space lower bounds for linear prediction in the streaming model COLT 2019