kernel regression

On Exact Computation with an Infinitely Wide Neural Net NeurIPS 2019 Sparse Kernel Regression with Coefficient-based ℓ_q-regularization JMLR 2019 Sublinear Time Numerical Linear Algebra for Structured Matrices AAAI 2019 Variational Inference of Penalized Regression with Submodular Functions UAI 2019 Beyond Similar and Dissimilar Relations : A Kernel Regression Formulation for Metric Learning IJCAI 2018 Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling ICML 2018 Learning and Inference in Hilbert Space with Quantum Graphical Models NeurIPS 2018 On b-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data JMLR 2018 Streaming kernel regression with provably adaptive mean, variance, and regularization JMLR 2018 Structured Gaussian Processes with Twin Multiple Kernel Learning ACML 2018 Generative Local Metric Learning for Kernel Regression NeurIPS 2017 Learning Theory of Distributed Regression with Bias Corrected Regularization Kernel Network JMLR 2017 Time-Accuracy Tradeoffs in Kernel Prediction: Controlling Prediction Quality JMLR 2017 A Nonlinear Regression Technique for Manifold Valued Data With Applications to Medical Image Analysis CVPR 2016 Gradients Weights improve Regression and Classification JMLR 2016 Multivariate Regression on the Grassmannian for Predicting Novel Domains CVPR 2016 Blind Image Quality Assessment using Semi-supervised Rectifier Networks CVPR 2014 Robust and Efficient Kernel Hyperparameter Paths with Guarantees ICML 2014 Theoretical Analyses on Ensemble and Multiple Kernel Regressors ACML 2014 Adaptivity to Local Smoothness and Dimension in Kernel Regression NeurIPS 2013 Correlated random features for fast semi-supervised learning NeurIPS 2013 Heterogeneous-Neighborhood-based Multi-Task Local Learning Algorithms NeurIPS 2013 Robust Data-Driven Dynamic Programming NeurIPS 2013 Subcategory-Aware Object Classification CVPR 2013 Gradient Weights help Nonparametric Regressors NeurIPS 2012