Maximum Mean Discrepancy Gradient Flow

11 June 2019

Papers citing "Maximum Mean Discrepancy Gradient Flow"

26 / 26 papers shown

Title
A Dictionary of Closed-Form Kernel Mean Embeddings F. Briol A. Gessner Toni Karvonen Maren Mahsereci BDL 78 1 0 26 Apr 2025
Ultra-fast feature learning for the training of two-layer neural networks in the two-timescale regime Raphael Barboni Gabriel Peyré François-Xavier Vialard MLT 34 0 0 25 Apr 2025
Flow Matching Ergodic Coverage Max Muchen Sun Allison Pinosky Todd Murphey 30 0 0 24 Apr 2025
DDEQs: Distributional Deep Equilibrium Models through Wasserstein Gradient Flows Jonathan Geuter Clément Bonet Anna Korba David Alvarez-Melis 56 0 0 03 Mar 2025
Non-geodesically-convex optimization in the Wasserstein space Hoang Phuc Hau Luu Hanlin Yu Bernardo Williams Petrus Mikkola Marcelo Hartmann Kai Puolamaki Arto Klami 53 2 0 08 Jan 2025
Fast Summation of Radial Kernels via QMC Slicing Johannes Hertrich Tim Jahn Michael Quellmalz 26 5 0 02 Oct 2024
Particle Semi-Implicit Variational Inference Jen Ning Lim A. M. Johansen 43 3 0 30 Jun 2024
Deep MMD Gradient Flow without adversarial training Alexandre Galashov Valentin De Bortoli Arthur Gretton DiffM 37 7 0 10 May 2024
GAD-PVI: A General Accelerated Dynamic-Weight Particle-Based Variational Inference Framework Fangyikang Wang Huminhao Zhu Chao Zhang Han Zhao Hui Qian 24 5 0 27 Dec 2023
Mean-field underdamped Langevin dynamics and its spacetime discretization Qiang Fu Ashia Wilson 34 4 0 26 Dec 2023
Differentially Private Gradient Flow based on the Sliced Wasserstein Distance Ilana Sebag Muni Sreenivas Pydi Jean-Yves Franceschi Alain Rakotomamonjy Mike Gartrell Jamal Atif Alexandre Allauzen 24 2 0 13 Dec 2023
Estimation Beyond Data Reweighting: Kernel Method of Moments Heiner Kremer Yassine Nemmour Bernhard Schölkopf Jia-Jie Zhu 31 7 0 18 May 2023
The Score-Difference Flow for Implicit Generative Modeling Romann M. Weber DiffM 29 2 0 25 Apr 2023
Block-wise Training of Residual Networks via the Minimizing Movement Scheme Skander Karkar Ibrahim Ayed Emmanuel de Bézenac Patrick Gallinari 27 1 0 03 Oct 2022
Membership Inference Attacks via Adversarial Examples Hamid Jalalzai Elie Kadoche Rémi Leluc Vincent Plassier AAML FedML MIACV 27 7 0 27 Jul 2022
A Deterministic Sampling Method via Maximum Mean Discrepancy Flow with Adaptive Kernel Yindong Chen Yiwei Wang Lulu Kang Chun Liu 15 1 0 21 Nov 2021
Efficient Gradient Flows in Sliced-Wasserstein Space Clément Bonet Nicolas Courty Franccois Septier Lucas Drumetz 29 21 0 21 Oct 2021
Dual Training of Energy-Based Models with Overparametrized Shallow Neural Networks Carles Domingo-Enrich A. Bietti Marylou Gabrié Joan Bruna Eric Vanden-Eijnden FedML 32 6 0 11 Jul 2021
Generalization Error of GAN from the Discriminator's Perspective Hongkang Yang Weinan E GAN 38 13 0 08 Jul 2021
A Neural Tangent Kernel Perspective of GANs Jean-Yves Franceschi Emmanuel de Bézenac Ibrahim Ayed Mickaël Chen Sylvain Lamprier Patrick Gallinari 29 26 0 10 Jun 2021
Kernel Stein Discrepancy Descent Anna Korba Pierre-Cyril Aubin-Frankowski Szymon Majewski Pierre Ablin 13 50 0 20 May 2021
Stein Variational Gradient Descent: many-particle and long-time asymptotics Nikolas Nusken D. M. Renger 21 22 0 25 Feb 2021
On the Convergence of Gradient Descent in GANs: MMD GAN As a Gradient Flow Youssef Mroueh Truyen V. Nguyen 23 25 0 04 Nov 2020
Can Shallow Neural Networks Beat the Curse of Dimensionality? A mean field training perspective Stephan Wojtowytsch E. Weinan MLT 21 48 0 21 May 2020
Statistical and Topological Properties of Sliced Probability Divergences Kimia Nadjahi Alain Durmus Lénaïc Chizat Soheil Kolouri Shahin Shahrampour Umut Simsekli 21 80 0 12 Mar 2020
Machine Learning from a Continuous Viewpoint E. Weinan Chao Ma Lei Wu 18 102 0 30 Dec 2019