kernel methods

Optimal Learning of Kernel Logistic Regression for Complex Classification Scenarios ICLR 2025 ProKeR: A Kernel Perspective on Few-Shot Adaptation of Large Vision-Language Models CVPR 2025 Relaxed Gaussian Process Interpolation: a Goal-Oriented Approach to Bayesian Optimization JMLR 2025 Reliable and Scalable Variable Importance Estimation via Warm-start and Early Stopping AISTATS 2025 Representer Theorems for Metric and Preference Learning: Geometric Insights and Algorithms AISTATS 2025 Robust Kernel Hypothesis Testing under Data Corruption AISTATS 2025 Skill Disentanglement in Reproducing Kernel Hilbert Space AAAI 2025 Statistical inference on black-box generative models in the data kernel perspective space ACL 2025 Tensor Network Based Feature Learning Model AISTATS 2025 The GeometricKernels Package: Heat and Matérn Kernels for Geometric Learning on Manifolds, Meshes, and Graphs JMLR 2025 The Z-Gromov-Wasserstein Distance JMLR 2025 Towards Understanding the Universality of Transformers for Next-Token Prediction ICLR 2025 Towards Unified Native Spaces in Kernel Methods JMLR 2025 Unsupervised Kernel-based Multi-view Feature Selection with Robust Self-representation and Binary Hashing AAAI 2025 Variance-Reducing Couplings for Random Features ICLR 2025 When does compositional structure yield compositional generalization? A kernel theory. ICLR 2025 Adversarial Contextual Bandits Go Kernelized ALT 2024 A General Framework for the Analysis of Kernel-based Tests JMLR 2024 A Kernel Test for Causal Association via Noise Contrastive Backdoor Adjustment JMLR 2024 A multi-modal distributed learning algorithm in reproducing kernel Hilbert spaces L4DC 2024 A New Neural Kernel Regime: The Inductive Bias of Multi-Task Learning NeurIPS 2024 A Rainbow in Deep Network Black Boxes JMLR 2024 A Specialized Semismooth Newton Method for Kernel-Based Optimal Transport AISTATS 2024 Causal Modeling with Stationary Diffusions AISTATS 2024 Cluster-wise Graph Transformer with Dual-granularity Kernelized Attention NeurIPS 2024