kernel mean embedding

IDK-S: Incremental Distributional Kernel for Streaming Anomaly Detection AAAI 2026 Kernel Mean Embedding Deviation Subspace for Unsupervised Learning with Heterogeneous Data JMLR 2026 Statistical Learning Theory for Distributional Classification AAAI 2026 AdMiT: Adaptive Multi-Source Tuning in Dynamic Environments CVPR 2025 Efficient Numerical Integration in Reproducing Kernel Hilbert Spaces via Leverage Scores Sampling JMLR 2025 Variance-Aware Estimation of Kernel Mean Embedding JMLR 2025 Characterization of translation invariant MMD on Rd and connections with Wasserstein distances JMLR 2024 Deep Hierarchical Graph Alignment Kernels IJCAI 2024 Differential Privacy in Scalable General Kernel Learning via K-means Nystr\"om Random Features NeurIPS 2024 On the Ability of Developers' Training Data Preservation of Learnware NeurIPS 2024 Recursive Estimation of Conditional Kernel Mean Embeddings JMLR 2024 Transfer learning for atomistic simulations using GNNs and kernel mean embeddings NeurIPS 2023 Deep Layer-wise Networks Have Closed-Form Weights AISTATS 2022 Distributionally-Aware Kernelized Bandit Problems for Risk Aversion ICML 2022 Distribution Regression with Sliced Wasserstein Kernels ICML 2022 Hermite Polynomial Features for Private Data Generation ICML 2022 Non-parametric inference of relational dependence UAI 2022 Nyström Kernel Mean Embeddings ICML 2022 RKHS-SHAP: Shapley Values for Kernel Methods NeurIPS 2022 BayesIMP: Uncertainty Quantification for Causal Data Fusion NeurIPS 2021 DP-MERF: Differentially Private Mean Embeddings with RandomFeatures for Practical Privacy-preserving Data Generation AISTATS 2021 High-Dimensional Multi-Task Averaging and Application to Kernel Mean Embedding AISTATS 2021 Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes NeurIPS 2021 Isolation Graph Kernel AAAI 2021 Noise Contrastive Meta-Learning for Conditional Density Estimation using Kernel Mean Embeddings AISTATS 2021