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Regression

5,004 papers

Papers per year

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Papers

Total Variation Classes Beyond 1d: Minimax Rates, and the Limitations of Linear Smoothers NIPS 2016
Optimal Black-Box Reductions Between Optimization Objectives NIPS 2016
Split LBI: An Iterative Regularization Path with Structural Sparsity NIPS 2016
Incremental Variational Sparse Gaussian Process Regression NIPS 2016
Greedy Feature Construction NIPS 2016
k*-Nearest Neighbors: From Global to Local NIPS 2016
Feature-distributed sparse regression: a screen-and-clean approach NIPS 2016
Low-Rank Regression with Tensor Responses NIPS 2016
Learning values across many orders of magnitude NIPS 2016
The Multiple Quantile Graphical Model NIPS 2016
GAP Safe Screening Rules for Sparse-Group Lasso NIPS 2016
Sparse Support Recovery with Non-smooth Loss Functions NIPS 2016
SURGE: Surface Regularized Geometry Estimation from a Single Image NIPS 2016
Blind Regression: Nonparametric Regression for Latent Variable Models via Collaborative Filtering NIPS 2016
Understanding Probabilistic Sparse Gaussian Process Approximations NIPS 2016
Structured Matrix Recovery via the Generalized Dantzig Selector NIPS 2016
Linear Feature Encoding for Reinforcement Learning NIPS 2016
Mixed Linear Regression with Multiple Components NIPS 2016
Temporal Regularized Matrix Factorization for High-dimensional Time Series Prediction NIPS 2016
Structured Sparse Regression via Greedy Hard Thresholding NIPS 2016
Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares NIPS 2016
The Limits of Learning with Missing Data NIPS 2016
On Graph Reconstruction via Empirical Risk Minimization: Fast Learning Rates and Scalability NIPS 2016
Error Analysis of Generalized Nyström Kernel Regression NIPS 2016
Fast and Flexible Monotonic Functions with Ensembles of Lattices NIPS 2016