kernel methods

Position: $C^*$-Algebraic Machine Learning $-$ Moving in a New Direction ICML 2024 Quantized Fourier and Polynomial Features for more Expressive Tensor Network Models AISTATS 2024 Random features approximation for control-affine systems L4DC 2024 RedEx: Beyond Fixed Representation Methods via Convex Optimization ALT 2024 Rethinking Backdoor Attacks on Dataset Distillation: A Kernel Method Perspective ICLR 2024 Rewrite the Stars CVPR 2024 Robust Contrastive Multi-view Kernel Clustering IJCAI 2024 Safe Reinforcement Learning with Instantaneous Constraints: The Role of Aggressive Exploration AAAI 2024 Sample-efficient Bayesian Optimisation Using Known Invariances NeurIPS 2024 Scalable Kernel Inverse Optimization NeurIPS 2024 SeTformer Is What You Need for Vision and Language AAAI 2024 Sketch In, Sketch Out: Accelerating both Learning and Inference for Structured Prediction with Kernels AISTATS 2024 Small coresets via negative dependence: DPPs, linear statistics, and concentration NeurIPS 2024 Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces JMLR 2024 Spectral Analysis of the Neural Tangent Kernel for Deep Residual Networks JMLR 2024 Spectral Regularized Kernel Goodness-of-Fit Tests JMLR 2024 Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence NeurIPS 2024 Statistical Optimality of Divide and Conquer Kernel-based Functional Linear Regression JMLR 2024 Stochastic Gradient Descent for Gaussian Processes Done Right ICLR 2024 Stochastic Kernel Regularisation Improves Generalisation in Deep Kernel Machines NeurIPS 2024 Supervised Kernel Thinning NeurIPS 2024 Temporal Graph Neural Tangent Kernel with Graphon-Guaranteed NeurIPS 2024 Testing Semantic Importance via Betting NeurIPS 2024 The Galerkin method beats Graph-Based Approaches for Spectral Algorithms AISTATS 2024 The Minimax Rate of HSIC Estimation for Translation-Invariant Kernels NeurIPS 2024