additive model

Generalized Boosting NeurIPS 2020 Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery NeurIPS 2020 Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models AISTATS 2020 A Refined Margin Distribution Analysis for Forest Representation Learning NeurIPS 2019 Explaining Neural Networks Semantically and Quantitatively ICCV 2019 Efficient High Dimensional Bayesian Optimization with Additivity and Quadrature Fourier Features NeurIPS 2018 High-Dimensional Bayesian Optimization via Additive Models with Overlapping Groups AISTATS 2018 Sparse Linear Isotonic Models AISTATS 2018 Additive Component Analysis CVPR 2017 Group Additive Structure Identification for Kernel Nonparametric Regression NeurIPS 2017 Group Sparse Additive Machine NeurIPS 2017 Additive Approximations in High Dimensional Nonparametric Regression via the SALSA ICML 2016 Chained Gaussian Processes AISTATS 2016 Learning Sparse Additive Models with Interactions in High Dimensions AISTATS 2016 High Dimensional Bayesian Optimisation and Bandits via Additive Models ICML 2015 Reducing the Rank in Relational Factorization Models by Including Observable Patterns NeurIPS 2014 A Theory of Multiclass Boosting JMLR 2013 Adaptive Learning of Smoothing Functions: Application to Electricity Load Forecasting NeurIPS 2012 Nonparametric Reduced Rank Regression NeurIPS 2012 PAC-Bayesian Bound for Gaussian Process Regression and Multiple Kernel Additive Model COLT 2012 Additive Gaussian Processes NeurIPS 2011 Model-based Boosting 2.0 JMLR 2010 Lower bounds on minimax rates for nonparametric regression with additive sparsity and smoothness NeurIPS 2009 Nonparametric Greedy Algorithms for the Sparse Learning Problem NeurIPS 2009 Nonparametric regression and classification with joint sparsity constraints NeurIPS 2008