Regression

Bundle Methods for Regularized Risk Minimization JMLR 2010 Conditional Density Estimation via Least-Squares Density Ratio Estimation AISTATS 2010 Decomposing Isotonic Regression for Efficiently Solving Large Problems NeurIPS 2010 Descent Methods for Tuning Parameter Refinement AISTATS 2010 Dimensionality Estimation, Manifold Learning and Function Approximation using Tensor Voting JMLR 2010 Direct Loss Minimization for Structured Prediction NeurIPS 2010 Dirichlet Process Mixtures of Generalized Linear Models AISTATS 2010 Efficient Multioutput Gaussian Processes through Variational Inducing Kernels AISTATS 2010 Estimation of a Structural Vector Autoregression Model Using Non-Gaussianity JMLR 2010 Exact learning curves for Gaussian process regression on large random graphs NeurIPS 2010 Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net AISTATS 2010 Fast Active-set-type Algorithms for l1-regularized Linear Regression AISTATS 2010 Fast detection of multiple change-points shared by many signals using group LARS NeurIPS 2010 Gaussian Processes for Machine Learning (GPML) Toolbox JMLR 2010 Gaussian processes with monotonicity information AISTATS 2010 Graph-Valued Regression NeurIPS 2010 Guaranteed Rank Minimization via Singular Value Projection NeurIPS 2010 Heavy-Tailed Process Priors for Selective Shrinkage NeurIPS 2010 Hierarchical Gaussian Process Regression ACML 2010 High-dimensional Variable Selection with Sparse Random Projections: Measurement Sparsity and Statistical Efficiency JMLR 2010 Image Denoising with Kernels Based on Natural Image Relations JMLR 2010 Kernel Partial Least Squares is Universally Consistent AISTATS 2010 Layered image motion with explicit occlusions, temporal consistency, and depth ordering NeurIPS 2010 Learning Exponential Families in High-Dimensions: Strong Convexity and Sparsity AISTATS 2010 Learning Networks of Stochastic Differential Equations NeurIPS 2010