Cover Image

A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chen, Yindong, Wang, Yiwei, Kang, Lulu, Liu, Chun
Format:	Preprint
Published:	2021
Subjects:	Machine Learning Computation
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Neural Wasserstein Gradient Flows for Maximum Mean Discrepancies with Riesz Kernels
by: Altekrüger, Fabian, et al.
Published: (2023)

(De)-regularized Maximum Mean Discrepancy Gradient Flow
by: Chen, Zonghao, et al.
Published: (2024)

Word Sense Detection Leveraging Maximum Mean Discrepancy
by: Mitsuzawa, Kensuke
Published: (2025)

MMD-Flagger: Leveraging Maximum Mean Discrepancy to Detect Hallucinations
by: Mitsuzawa, Kensuke, et al.
Published: (2025)

Sample-Efficient Human Evaluation of Large Language Models via Maximum Discrepancy Competition
by: Feng, Kehua, et al.
Published: (2024)

Signature Maximum Mean Discrepancy Two-Sample Statistical Tests
by: Alden, Andrew, et al.
Published: (2025)

M3D: Dataset Condensation by Minimizing Maximum Mean Discrepancy
by: Zhang, Hansong, et al.
Published: (2023)

On the Optimization Landscape of Maximum Mean Discrepancy
by: Alon, Itai, et al.
Published: (2021)

Particle-based Energetic Variational Inference
by: Wang, Yiwei, et al.
Published: (2020)

Maximum Mean Discrepancy with Unequal Sample Sizes via Generalized U-Statistics
by: Wei, Aaron, et al.
Published: (2025)

Optimizing Kernel Discrepancies via Subset Selection
by: Chen, Deyao, et al.
Published: (2025)

Detecting Machine-Generated Texts by Multi-Population Aware Optimization for Maximum Mean Discrepancy
by: Zhang, Shuhai, et al.
Published: (2024)

HistoKernel: Whole Slide Image Level Maximum Mean Discrepancy Kernels for Pan-Cancer Predictive Modelling
by: Keller, Piotr, et al.
Published: (2024)

Training-Free Distribution Adaptation for Diffusion Models via Maximum Mean Discrepancy Guidance
by: Sani, Matina Mahdizadeh, et al.
Published: (2026)

Ensemble Transport Filter via Optimized Maximum Mean Discrepancy
by: Zeng, Dengfei, et al.
Published: (2024)

Controlling Moments with Kernel Stein Discrepancies
by: Kanagawa, Heishiro, et al.
Published: (2022)

Convolutional Maximum Mean Discrepancy for Inference in Noisy Data
by: Vashistha, Ritwik, et al.
Published: (2026)

Variable Selection in Maximum Mean Discrepancy for Interpretable Distribution Comparison
by: Mitsuzawa, Kensuke, et al.
Published: (2023)

Active Learning for Manifold Gaussian Process Regression
by: Cheng, Yuanxing, et al.
Published: (2025)

MMD-OPT : Maximum Mean Discrepancy Based Sample Efficient Collision Risk Minimization for Autonomous Driving
by: Sharma, Basant, et al.
Published: (2024)

Data-centric Prediction Explanation via Kernelized Stein Discrepancy
by: Sarvmaili, Mahtab, et al.
Published: (2024)

Sampling in Unit Time with Kernel Fisher-Rao Flow
by: Maurais, Aimee, et al.
Published: (2024)

Stochastic Sampling from Deterministic Flow Models
by: Singh, Saurabh, et al.
Published: (2024)

Cross-Modal Domain Adaptation in Brain Disease Diagnosis: Maximum Mean Discrepancy-based Convolutional Neural Networks
by: Zhu, Xuran
Published: (2024)

Scalable Batch Correction for Cell Painting via Batch-Dependent Kernels and Adaptive Sampling
by: Ravi, Aditya Narayan, et al.
Published: (2026)

A Bayesian Non-parametric Approach to Generative Models: Integrating Variational Autoencoder and Generative Adversarial Networks using Wasserstein and Maximum Mean Discrepancy
by: Fazeli-Asl, Forough, et al.
Published: (2023)

Quantitative Convergence of Wasserstein Gradient Flows of Kernel Mean Discrepancies
by: Chizat, Lénaïc, et al.
Published: (2026)

Arbitrary Distributions Mapping via SyMOT-Flow: A Flow-based Approach Integrating Maximum Mean Discrepancy and Optimal Transport
by: Xiong, Zhe, et al.
Published: (2023)

Utilizing Maximum Mean Discrepancy Barycenter for Propagating the Uncertainty of Value Functions in Reinforcement Learning
by: Roy, Srinjoy, et al.
Published: (2024)

Accelerating Particle-based Energetic Variational Inference
by: Bao, Xuelian, et al.
Published: (2025)

Robust and Sparse Generalized Linear Models for High-Dimensional Data via Maximum Mean Discrepancy
by: Kang, Xiaoning, et al.
Published: (2026)

Bias Detection via Maximum Subgroup Discrepancy
by: Němeček, Jiří, et al.
Published: (2025)

Learning Dynamic Representations via An Optimally-Weighted Maximum Mean Discrepancy Optimization Framework for Continual Learning
by: Huang, KaiHui, et al.
Published: (2025)

Conditional Distribution Compression via the Kernel Conditional Mean Embedding
by: Broadbent, Dominic, et al.
Published: (2025)

Maximum Mean Discrepancy on Exponential Windows for Online Change Detection
by: Kalinke, Florian, et al.
Published: (2022)

Sequential Kernelized Stein Discrepancy
by: Martinez-Taboada, Diego, et al.
Published: (2024)

KerJEPA: Kernel Discrepancies for Euclidean Self-Supervised Learning
by: Zimmermann, Eric, et al.
Published: (2025)

Anchor-based Maximum Discrepancy for Relative Similarity Testing
by: Zhou, Zhijian, et al.
Published: (2025)

Fair Multivariate Adaptive Regression Splines for Ensuring Equity and Transparency
by: Haghighat, Parian, et al.
Published: (2024)

An Adaptive Importance Sampling for Locally Stable Point Processes
by: Kang, Hee-Geon, et al.
Published: (2024)