representer theorem

Nonparametric estimation of Hawkes processes with RKHSs AISTATS 2025 On Support Samples of Next Word Prediction ACL 2025 Representer Theorems for Metric and Preference Learning: Geometric Insights and Algorithms AISTATS 2025 Variation Spaces for Multi-Output Neural Networks: Insights on Multi-Task Learning and Network Compression JMLR 2024 Learning Partial Differential Equations in Reproducing Kernel Hilbert Spaces JMLR 2023 Representer Point Selection for Explaining Regularized High-dimensional Models ICML 2023 Fast Bayesian Estimation of Point Process Intensity as Function of Covariates NeurIPS 2022 Banach Space Representer Theorems for Neural Networks and Ridge Splines JMLR 2021 Causal Modeling with Stochastic Confounders AISTATS 2021 Learning Finite-Dimensional Representations For Koopman Operators L4DC 2021 Nonparametric estimation of continuous DPPs with kernel methods NeurIPS 2021 Representer Theorems in Banach Spaces: Minimum Norm Interpolation, Regularized Learning and Semi-Discrete Inverse Problems JMLR 2021 Reproducing kernel Hilbert C*-module and kernel mean embeddings JMLR 2021 Approximate Representer Theorems in Non-reflexive Banach Spaces ALT 2020 Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces NeurIPS 2020 Non-parametric Models for Non-negative Functions NeurIPS 2020 A Representer Theorem for Deep Kernel Learning JMLR 2019 A Representer Theorem for Deep Neural Networks JMLR 2019 Learning in Reproducing Kernel Kreı̆n Spaces ICML 2018 Operator-valued Kernels for Learning from Functional Response Data JMLR 2016 A Unifying View of Representer Theorems ICML 2014 Characterizing the Representer Theorem ICML 2013 Learning with Invariance via Linear Functionals on Reproducing Kernel Hilbert Space NeurIPS 2013 The representer theorem for Hilbert spaces: a necessary and sufficient condition NeurIPS 2012 Nonlinear functional regression: a functional RKHS approach AISTATS 2010