Regression

Gradient-based Sampling: An Adaptive Importance Sampling for Least-squares NeurIPS 2016 Gradients Weights improve Regression and Classification JMLR 2016 Greedy Feature Construction NeurIPS 2016 Guarding against Spurious Discoveries in High Dimensions JMLR 2016 Heteroscedastic Sequences: Beyond Gaussianity ICML 2016 High resolution neural connectivity from incomplete tracing data using nonnegative spline regression NeurIPS 2016 Highway Vehicle Counting in Compressed Domain CVPR 2016 How Hard Can It Be? Estimating the Difficulty of Visual Search in an Image CVPR 2016 HyperDepth: Learning Depth From Structured Light Without Matching CVPR 2016 Identifying Good Training Data for Self-Supervised Free Space Estimation CVPR 2016 Incremental Variational Sparse Gaussian Process Regression NeurIPS 2016 Input Output Kernel Regression: Supervised and Semi-Supervised Structured Output Prediction with Operator-Valued Kernels JMLR 2016 Input-Output Non-Linear Dynamical Systems applied to Physiological Condition Monitoring MLHC 2016 Integrative Analysis using Coupled Latent Variable Models for Individualizing Prognoses JMLR 2016 Isotonic Hawkes Processes ICML 2016 Joint quantile regression in vector-valued RKHSs NeurIPS 2016 Joint Structural Estimation of Multiple Graphical Models JMLR 2016 k*-Nearest Neighbors: From Global to Local NeurIPS 2016 L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs JMLR 2016 L1-regularized Neural Networks are Improperly Learnable in Polynomial Time ICML 2016 Large Scale Online Kernel Learning JMLR 2016 Large-Scale Optimization Algorithms for Sparse Conditional Gaussian Graphical Models AISTATS 2016 Learning from Multiway Data: Simple and Efficient Tensor Regression ICML 2016 Learning Mixtures of Plackett-Luce Models ICML 2016 Learning Online Smooth Predictors for Realtime Camera Planning Using Recurrent Decision Trees CVPR 2016