deep kernel learning

DKMGP: A Gaussian Process Approach to Multi-Task and Multi-Step Vehicle Dynamics Modeling in Autonomous Racing L4DC 2025 Error Bounds for Gaussian Process Regression Under Bounded Support Noise with Applications to Safety Certification AAAI 2025 From Deep Additive Kernel Learning to Last-Layer Bayesian Neural Networks via Induced Prior Approximation AISTATS 2025 Robust Multi-fidelity Bayesian Optimization with Deep Kernel and Partition AISTATS 2025 Nonstationary Sparse Spectral Permanental Process NeurIPS 2024 Deep Kernel Regression with Finite Learnable Kernels ACML 2023 Deep learning with kernels through RKHM and the Perron-Frobenius operator NeurIPS 2023 Guided Deep Kernel Learning UAI 2023 Hierarchical Kernels in Deep Kernel Learning JMLR 2023 Interactive Segmentation As Gaussion Process Classification CVPR 2023 Nearly-tight Bounds for Deep Kernel Learning ICML 2023 Scalable Deep Kernel Gaussian Process for Vehicle Dynamics in Autonomous Racing CoRL 2023 Physics Informed Deep Kernel Learning AISTATS 2022 Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning AISTATS 2021 GP-Tree: A Gaussian Process Classifier for Few-Shot Incremental Learning ICML 2021 Longitudinal Deep Kernel Gaussian Process Regression AAAI 2021 Personalized Federated Learning With Gaussian Processes NeurIPS 2021 The promises and pitfalls of deep kernel learning UAI 2021 Deep Message Passing on Sets AAAI 2020 Stepwise Model Selection for Sequence Prediction via Deep Kernel Learning AISTATS 2020 A Representer Theorem for Deep Kernel Learning JMLR 2019 Deep Spectral Kernel Learning IJCAI 2019 Semi-supervised Deep Kernel Learning: Regression with Unlabeled Data by Minimizing Predictive Variance NeurIPS 2018 Deep Kernel Learning AISTATS 2016 Stochastic Variational Deep Kernel Learning NeurIPS 2016