kernel learning

Frequency Dynamic Convolution for Dense Image Prediction CVPR 2025 Kernel Learning for Sample Constrained Black-Box Optimization AAAI 2025 A Data-Adaptive RKHS Prior for Bayesian Learning of Kernels in Operators JMLR 2024 Differential Privacy in Scalable General Kernel Learning via K-means Nystr\"om Random Features NeurIPS 2024 Distributed Kernel-Driven Data Clustering JMLR 2024 Efficient Convex Algorithms for Universal Kernel Learning JMLR 2024 Fast Fourier Bayesian Quadrature AISTATS 2024 Learning Adaptive Kernels for Statistical Independence Tests AISTATS 2024 Learning GAI-Decomposable Utility Models for Multiattribute Decision Making AAAI 2024 Post-Regularization Confidence Bands for Ordinary Differential Equations JMLR 2024 Amortized Inference for Gaussian Process Hyperparameters of Structured Kernels UAI 2023 Bayesian Optimisation of Functions on Graphs NeurIPS 2023 Optimal Convergence Rates for Agnostic Nyström Kernel Learning ICML 2023 Optimal Convergence Rates for Distributed Nystroem Approximation JMLR 2023 Set-valued Classification with Out-of-distribution Detection for Many Classes JMLR 2023 Thin and deep Gaussian processes NeurIPS 2023 Empowering parameter-efficient transfer learning by recognizing the kernel structure in self-attention NAACL 2022 Evolution of Neural Tangent Kernels under Benign and Adversarial Training NeurIPS 2022 Marginalising over Stationary Kernels with Bayesian Quadrature AISTATS 2022 Meta-Learning Hypothesis Spaces for Sequential Decision-making ICML 2022 TRF: Learning Kernels with Tuned Random Features AAAI 2022 Video K-Net: A Simple, Strong, and Unified Baseline for Video Segmentation CVPR 2022 A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance NeurIPS 2021 Distributed Nyström Kernel Learning with Communications ICML 2021 Entangled Kernels - Beyond Separability JMLR 2021