Implicit Kernel Learning

26 February 2019

Chun-Liang Li

Papers citing "Implicit Kernel Learning"

25 / 25 papers shown

Title
CKGAN: Training Generative Adversarial Networks Using Characteristic Kernel Integral Probability Metrics Kuntian Zhang Simin Yu Yaoshu Wang Makoto Onizuka Chuan Xiao GAN 96 0 0 08 Apr 2025
Hyperparameter Optimization for Randomized Algorithms: A Case Study on Random Features Oliver R. A. Dunbar Nicholas H. Nelsen Maya Mutic 145 7 0 30 Jun 2024
On the Laplace Approximation as Model Selection Criterion for Gaussian Processes Andreas Besginow J. D. Hüwel Thomas Pawellek Christian Beecks Markus Lange-Hegermann 35 0 0 14 Mar 2024
Asymptotic Theory for Linear Functionals of Kernel Ridge Regression Rui Tuo Lu Zou 55 0 0 07 Mar 2024
Wasserstein Distortion: Unifying Fidelity and Realism Yang Qiu Aaron B. Wagner Johannes Ballé Lucas Theis 83 6 0 05 Oct 2023
Kernel Biclustering algorithm in Hilbert Spaces Marcos Matabuena J. Vidal Oscar Hernan Madrid Padilla Dino Sejdinovic 43 2 0 07 Aug 2022
On Learning the Transformer Kernel Sankalan Pal Chowdhury Adamos Solomou Kumar Avinava Dubey Mrinmaya Sachan ViT 131 14 0 15 Oct 2021
Diverse Sample Generation: Pushing the Limit of Generative Data-free Quantization Haotong Qin Yifu Ding Xiangguo Zhang Jiakai Wang Xianglong Liu Jiwen Lu DiffM MQ 56 57 0 01 Sep 2021
Learning High-Dimensional Distributions with Latent Neural Fokker-Planck Kernels Yufan Zhou Changyou Chen Jinhui Xu 39 2 0 10 May 2021
MetaKernel: Learning Variational Random Features with Limited Labels Yingjun Du Haoliang Sun Xiantong Zhen Jun Xu Yilong Yin Ling Shao Cees G. M. Snoek VLM BDL 37 5 0 08 May 2021
Kernel-Based Models for Influence Maximization on Graphs based on Gaussian Process Variance Minimization S. Cuomo W. Erb G. Santin 70 3 0 02 Mar 2021
Learning with Density Matrices and Random Features Fabio A. González Joseph A. Gallego-Mejia Santiago Toledo-Cortés Vladimir Vargas-Calderón 60 29 0 08 Feb 2021
Reducing the Variance of Variational Estimates of Mutual Information by Limiting the Critic's Hypothesis Space to RKHS P. A. Sreekar Ujjwal Tiwari A. Namboodiri 36 2 0 17 Nov 2020
Learning Manifold Implicitly via Explicit Heat-Kernel Learning Yufan Zhou Changyou Chen Jinhui Xu 73 8 0 05 Oct 2020
End-to-end Kernel Learning via Generative Random Fourier Features Kun Fang Fanghui Liu Xiaolin Huang Jie Yang 36 9 0 10 Sep 2020
Kernel Stein Generative Modeling Wei-Cheng Chang Chun-Liang Li Youssef Mroueh Yiming Yang DiffM BDL 120 5 0 06 Jul 2020
Learning to Learn Kernels with Variational Random Features Xiantong Zhen Hao Sun Yingjun Du Jun Xu Yilong Yin Ling Shao Cees G. M. Snoek DRL 72 34 0 11 Jun 2020
Implicit Kernel Attention Kyungwoo Song Yohan Jung Dongjun Kim Il-Chul Moon 76 16 0 11 Jun 2020
Random Features for Kernel Approximation: A Survey on Algorithms, Theory, and Beyond Fanghui Liu Xiaolin Huang Yudong Chen Johan A. K. Suykens BDL 126 176 0 23 Apr 2020
KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling Yufan Zhou Changyou Chen Jinhui Xu 33 2 0 02 Dec 2019
A Mean-Field Theory for Kernel Alignment with Random Features in Generative and Discriminative Models M. B. Khuzani Liyue Shen Shahin Shahrampour Lei Xing 50 1 0 25 Sep 2019
A Characteristic Function Approach to Deep Implicit Generative Modeling Abdul Fatir Ansari Jonathan Scarlett Harold Soh VLM GAN 53 40 0 16 Sep 2019
Generalization Properties of hyper-RKHS and its Applications Fanghui Liu Lei Shi Xiaolin Huang Jie Yang Johan A. K. Suykens 46 4 0 26 Sep 2018
Learning Data-adaptive Nonparametric Kernels Fanghui Liu Xiaolin Huang Chen Gong Jie Yang Li Li 81 18 0 31 Aug 2018
Generative Ratio Matching Networks Akash Srivastava Kai Xu Michael U. Gutmann Charles Sutton GAN 58 12 0 31 May 2018