Regression

Augmented Functional Time Series Representation and Forecasting with Gaussian Processes NeurIPS 2007 Better than least squares: comparison of objective functions for estimating linear-nonlinear models NeurIPS 2007 Blind channel identification for speech dereverberation using l1-norm sparse learning NeurIPS 2007 Boosted Classification Trees and Class Probability/Quantile Estimation JMLR 2007 Bundle Methods for Machine Learning NeurIPS 2007 Catching Change-points with Lasso NeurIPS 2007 Characterizing the Function Space for Bayesian Kernel Models JMLR 2007 Compressed Regression NeurIPS 2007 Density Estimation under Independent Similarly Distributed Sampling Assumptions NeurIPS 2007 Direct Importance Estimation with Model Selection and Its Application to Covariate Shift Adaptation NeurIPS 2007 Efficient multiple hyperparameter learning for log-linear models NeurIPS 2007 Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization NeurIPS 2007 Evaluating Search Engines by Modeling the Relationship Between Relevance and Clicks NeurIPS 2007 Feature Selection Methods for Improving Protein Structure Prediction with Rosetta NeurIPS 2007 FilterBoost: Regression and Classification on Large Datasets NeurIPS 2007 Hierarchical Average Reward Reinforcement Learning JMLR 2007 Hierarchical Penalization NeurIPS 2007 How SVMs can estimate quantiles and the median NeurIPS 2007 Learnability of Gaussians with Flexible Variances JMLR 2007 Locality and low-dimensions in the prediction of natural experience from fMRI NeurIPS 2007 Non-parametric Modeling of Partially Ranked Data NeurIPS 2007 Online Linear Regression and Its Application to Model-Based Reinforcement Learning NeurIPS 2007 On Ranking in Survival Analysis: Bounds on the Concordance Index NeurIPS 2007 On the Representer Theorem and Equivalent Degrees of Freedom of SVR JMLR 2007 Predicting Brain States from fMRI Data: Incremental Functional Principal Component Regression NeurIPS 2007