kernel approximation

Balancing Expressivity and Learnability in Quantum Kernel Bandit Optimization UAI 2026 Learning Expressive Random Feature Models via Parametrized Activations UAI 2026 PUNO: A Neural Operator Framework for Point Cloud Upsampling AAAI 2026 Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method AAAI 2025 Fast Second-Order Online Kernel Learning Through Incremental Matrix Sketching and Decomposition IJCAI 2025 Incremental Nyström-based Multiple Kernel Clustering AAAI 2025 On the Approximation of Kernel functions JMLR 2025 Optimized Random Features for the Neural Tangent Kernel (Student Abstract) AAAI 2025 General Graph Random Features ICLR 2024 Improved Random Features for Dot Product Kernels JMLR 2024 Locality-Sensitive Hashing-Based Efficient Point Transformer with Applications in High-Energy Physics ICML 2024 Nonstationary Sparse Spectral Permanental Process NeurIPS 2024 Quasi-Monte Carlo Features for Kernel Approximation ICML 2024 Sobolev Space Regularised Pre Density Models ICML 2024 Stein Random Feature Regression UAI 2024 Trigonometric Quadrature Fourier Features for Scalable Gaussian Process Regression AISTATS 2024 A Fast, Well-Founded Approximation to the Empirical Neural Tangent Kernel ICML 2023 Classically Approximating Variational Quantum Machine Learning with Random Fourier Features ICLR 2023 Dense-Exponential Random Features: Sharp Positive Estimators of the Gaussian Kernel NeurIPS 2023 Error Estimation for Random Fourier Features AISTATS 2023 Heteroscedastic Gaussian Processes and Random Features: Scalable Motion Primitives with Guarantees CoRL 2023 LoNe Sampler: Graph Node Embeddings by Coordinated Local Neighborhood Sampling AAAI 2023 Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression ICML 2023 Optimal Convergence Rates for Agnostic Nyström Kernel Learning ICML 2023 Quasi-Monte Carlo Graph Random Features NeurIPS 2023