reproducing kernel hilbert space
Reproducing kernel Hilbert space (RKHS) is a framework in functional analysis that extends Hilbert spaces to include kernels, enabling the representation of points in infinite-dimensional spaces. It is fundamental in many machine learning methods, particularly those using kernel machines.
- Convergence Rates of Constrained Expected Improvement
- Doubly-Robust Estimation of Counterfactual Policy Mean Embeddings
- Dynamic Regret Reduces to Kernelized Static Regret
- Gaussian Process Upper Confidence Bound Achieves Nearly-Optimal Regret in Noise-Free Gaussian Process Bandits
- Inverse Methods for Missing Data Imputation
- Kernel von Mises Formula of the Influence Function
- Learning Theory for Kernel Bilevel Optimization
- Support Vector Generation: Kernelizing Large Language Models for Efficient Zero‑Shot NLP
- Transfer Faster, Price Smarter: Minimax Dynamic Pricing under Cross-Market Preference Shift