maximum mean discrepancy

A Distributional Analogue to the Successor Representation ICML 2024 An Independence-promoting Loss for Music Generation with Language Models ICML 2024 Characterization of translation invariant MMD on Rd and connections with Wasserstein distances JMLR 2024 Data Distribution Valuation NeurIPS 2024 Debiased Distribution Compression ICML 2024 Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy ICLR 2024 Domain Adaptive 3D Shape Retrieval From Monocular Images WACV 2024 Domain Generalisation via Risk Distribution Matching WACV 2024 Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection NeurIPS 2024 Evaluating Transferability in Retrieval Tasks: An Approach Using MMD and Kernel Methods CVPR 2024 Fair Representation Learning with Maximum Mean Discrepancy Distance Constraint (Student Abstract) AAAI 2024 Generative Sliced MMD Flows with Riesz Kernels ICLR 2024 Graph Classification via Reference Distribution Learning: Theory and Practice NeurIPS 2024 Improving Equivariant Graph Neural Networks on Large Geometric Graphs via Virtual Nodes Learning ICML 2024 Interaction-Force Transport Gradient Flows NeurIPS 2024 Kernel Thinning JMLR 2024 M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy AAAI 2024 MMD-based Variable Importance for Distributional Random Forest AISTATS 2024 MMD Graph Kernel: Effective Metric Learning for Graphs via Maximum Mean Discrepancy ICLR 2024 Nickel and Diming Your GAN: A Dual-Method Approach to Enhancing GAN Efficiency via Knowledge Distillation ECCV 2024 Partial identification of the maximum mean discrepancy with mismeasured data UAI 2024 Posterior Sampling Based on Gradient Flows of the MMD with Negative Distance Kernel ICLR 2024 Rethinking Centered Kernel Alignment in Knowledge Distillation IJCAI 2024 Sample Complexity Bounds for Estimating Probability Divergences under Invariances ICML 2024 Spectral Regularized Kernel Goodness-of-Fit Tests JMLR 2024