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Kernel Methods

642 papers

Papers per year

9
23
20
31
19
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Papers

CosNet: A Generalized Spectral Kernel Network NIPS 2023
GEQ: Gaussian Kernel Inspired Equilibrium Models NIPS 2023
Deep learning with kernels through RKHM and the Perron-Frobenius operator NIPS 2023
Improved Kernel Alignment Regret Bound for Online Kernel Learning AAAI 2023
Symphony in the Latent Space: Provably Integrating High-Dimensional Techniques with Non-linear Machine Learning Models AAAI 2023
Practical Markov Boundary Learning without Strong Assumptions AAAI 2023
Error Estimation for Random Fourier Features AISTATS 2023
Sample Complexity of Kernel-Based Q-Learning AISTATS 2023
Isotropic Gaussian Processes on Finite Spaces of Graphs AISTATS 2023
Complex-to-Real Sketches for Tensor Products with Applications to the Polynomial Kernel AISTATS 2023
Tensor-based Kernel Machines with Structured Inducing Points for Large and High-Dimensional Data AISTATS 2023
Overparameterized Random Feature Regression with Nearly Orthogonal Data AISTATS 2023
Scalable Unbalanced Sobolev Transport for Measures on a Graph AISTATS 2023
GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-Shot Class Incremental Task CVPR 2023
Toward Large Kernel Models ICML 2023
A Statistical Perspective on Retrieval-Based Models ICML 2023
Kernel Logistic Regression Approximation of an Understandable ReLU Neural Network ICML 2023
Sparse Learning of Dynamical Systems in RKHS: An Operator-Theoretic Approach ICML 2023
Nearly Optimal Algorithms with Sublinear Computational Complexity for Online Kernel Regression ICML 2023
Optimal Convergence Rates for Agnostic Nyström Kernel Learning ICML 2023
Consistency of Multiple Kernel Clustering ICML 2023
Simplex Random Features ICML 2023
Kernel QuantTree ICML 2023
Extending Kernel PCA through Dualization: Sparsity, Robustness and Fast Algorithms ICML 2023
Nearly-tight Bounds for Deep Kernel Learning ICML 2023