conftrace_
← Core Methods
Machine Learning › Core Methods ›

Regression

4,964 papers

Papers per year

Papers

Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery NIPS 2019
Distributionally Robust Optimization and Generalization in Kernel Methods NIPS 2019
Kernel Instrumental Variable Regression NIPS 2019
Globally Optimal Learning for Structured Elliptical Losses NIPS 2019
Asymptotics for Sketching in Least Squares Regression NIPS 2019
Gradient Dynamics of Shallow Univariate ReLU Networks NIPS 2019
Causal Regularization NIPS 2019
Learning Neural Networks with Adaptive Regularization NIPS 2019
Sample Complexity of Learning Mixture of Sparse Linear Regressions NIPS 2019
Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization NIPS 2019
The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares NIPS 2019
iSplit LBI: Individualized Partial Ranking with Ties via Split LBI NIPS 2019
Personalizing Many Decisions with High-Dimensional Covariates NIPS 2019
Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso NIPS 2019
Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space NIPS 2019
Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models NIPS 2019
Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression NIPS 2019
Fast and Accurate Least-Mean-Squares Solvers NIPS 2019
Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks NIPS 2019
Large-scale optimal transport map estimation using projection pursuit NIPS 2019
Sparse High-Dimensional Isotonic Regression NIPS 2019
Dynamic Local Regret for Non-convex Online Forecasting NIPS 2019
Write, Execute, Assess: Program Synthesis with a REPL NIPS 2019
Conformalized Quantile Regression NIPS 2019
Learning Sample-Specific Models with Low-Rank Personalized Regression NIPS 2019