Regression

Predicting Partial Paths from Planning Problem Parameters RSS 2007 Predictive Matrix-Variate t Models NeurIPS 2007 Robust Regression with Twinned Gaussian Processes NeurIPS 2007 SpAM: Sparse Additive Models NeurIPS 2007 Stagewise Lasso JMLR 2007 Supervised Topic Models NeurIPS 2007 Using Deep Belief Nets to Learn Covariance Kernels for Gaussian Processes NeurIPS 2007 A Bayesian Approach to Nonlinear Parameter Identification for Rigid Body Dynamics RSS 2006 Active Learning in Approximately Linear Regression Based on Conditional Expectation of Generalization Error JMLR 2006 A Gravity Balancing Passive Exoskeleton for the Human Leg RSS 2006 A Humanlike Predictor of Facial Attractiveness NeurIPS 2006 A Kernel Subspace Method by Stochastic Realization for Learning Nonlinear Dynamical Systems NeurIPS 2006 A Scalable Machine Learning Approach to Go NeurIPS 2006 A Very Fast Learning Method for Neural Networks Based on Sensitivity Analysis JMLR 2006 Bayesian Ensemble Learning NeurIPS 2006 Gaussian Processes for Signal Strength-Based Location Estimation RSS 2006 Generalized Regularized Least-Squares Learning with Predefined Features in a Hilbert Space NeurIPS 2006 Infinite-σ Limits For Tikhonov Regularization JMLR 2006 Large Margin Multi-channel Analog-to-Digital Conversion with Applications to Neural Prosthesis NeurIPS 2006 Large-Scale Sparsified Manifold Regularization NeurIPS 2006 Learning Coordinate Covariances via Gradients JMLR 2006 Learning from Multiple Sources NeurIPS 2006 Learning Motion Style Synthesis from Perceptual Observations NeurIPS 2006 Logistic Regression for Single Trial EEG Classification NeurIPS 2006 Mixture Regression for Covariate Shift NeurIPS 2006