kernel methods

Anchor-Driven Nyström for Deep Graph-Level Clustering AAAI 2026 Benign Overfitting with Quantum Kernels UAI 2026 Defensive Generation COLT 2026 DETONATE – A Benchmark for Text-to-Image Alignment and Kernelized Direct Preference Optimization AAAI 2026 Distributional Deep Gaussian Processes UAI 2026 Dual-Kernel Graph Community Contrastive Learning AAAI 2026 Function-on-Function Bayesian Optimization AAAI 2026 Gaussian Process Limit Reveals Structural Benefits of Graph Transformers UAI 2026 Kernelized Edge Attention: Addressing Semantic Attention Blurring in Temporal Graph Neural Networks AAAI 2026 Koopman Operator for Stability Analysis: Theory with a Linear–Radial Product Reproducing Kernel L4DC 2026 Margin in Abstract Spaces COLT 2026 MoE^2: A Mixture-of-Mixtures of Experts for Ensemble-Free Domain Generalization AAAI 2026 Nonlinear function-on-function regression by RKHS JMLR 2026 Practical Global and Local Bounds in Gaussian Process Regression via Chaining AAAI 2026 Quantum Non-Linear Bandit Optimization AAAI 2026 Sparse Nonparametric Contextual Bandits ALT 2026 A Robust Kernel Statistical Test of Invariance: Detecting Subtle Asymmetries AISTATS 2025 Bayesian Treatment of the Spectrum of the Empirical Kernel in (Sub)Linear-Width Neural Networks ICLR 2025 Biological Sequence Kernels with Guaranteed Flexibility JMLR 2025 Bridging the Gap Between Hyperdimensional Computing and Kernel Methods via the Nyström Method AAAI 2025 Collaborative likelihood-ratio estimation over graphs JMLR 2025 Collaborative non-parametric two-sample testing AISTATS 2025 Composite Goodness-of-fit Tests with Kernels JMLR 2025 Continual Learning Using a Kernel-Based Method Over Foundation Models AAAI 2025 Correlation-Aware Example Selection for In-Context Learning with Nonsymmetric Determinantal Point Processes EMNLP 2025