Regression

Learning Nonlinear Dynamic Models from Non-sequenced Data AISTATS 2010 Learning sparse dynamic linear systems using stable spline kernels and exponential hyperpriors NeurIPS 2010 Learning To Count Objects in Images NeurIPS 2010 Matrix-Variate Dirichlet Process Mixture Models AISTATS 2010 Mixture of time-warped trajectory models for movement decoding NeurIPS 2010 Model-based Boosting 2.0 JMLR 2010 Multi-Stage Dantzig Selector NeurIPS 2010 Multi-task Learning for Recommender System ACML 2010 Multivariate Dyadic Regression Trees for Sparse Learning Problems NeurIPS 2010 Nonlinear functional regression: a functional RKHS approach AISTATS 2010 On the Impact of Kernel Approximation on Learning Accuracy AISTATS 2010 On the Rate of Convergence of the Bagged Nearest Neighbor Estimate JMLR 2010 Optimal learning rates for Kernel Conjugate Gradient regression NeurIPS 2010 Parametric Bandits: The Generalized Linear Case NeurIPS 2010 Penalized Principal Component Regression on Graphs for Analysis of Subnetworks NeurIPS 2010 Predicting Execution Time of Computer Programs Using Sparse Polynomial Regression NeurIPS 2010 Predictive State Temporal Difference Learning NeurIPS 2010 Regularized Discriminant Analysis, Ridge Regression and Beyond JMLR 2010 Relaxed Clipping: A Global Training Method for Robust Regression and Classification NeurIPS 2010 Rescaling, thinning or complementing? On goodness-of-fit procedures for point process models and Generalized Linear Models NeurIPS 2010 Restricted Eigenvalue Properties for Correlated Gaussian Designs JMLR 2010 Scrambled Objects for Least-Squares Regression NeurIPS 2010 Sparse Spectrum Gaussian Process Regression JMLR 2010 Stability Approach to Regularization Selection (StARS) for High Dimensional Graphical Models NeurIPS 2010 Stochastic Composite Likelihood JMLR 2010