Jingfeng Wu

I am a first year Ph.D. student at Johns Hopkins University, Computer Science Department. I work with Prof. Vladimir Braverman.

Previously, I obtained my B.S. and M.S. at Peking University, School of Mathematical Sciences. During my Masters period, I was luckily to be supervised by Prof. Jinwen Ma and Prof. Zhanxing Zhu.

Email  /  CV  /  Google Scholar  /  Github

• One paper is accepted to ICML-2019: "The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects".
• Our paper "Tangent-Normal Adversarial Regularization for Semi-supervised Learning" is accepted as an oral presentation to CVPR-2019!

I'm interested in both the theoretical and applied parts of machine learning. Currently, I am following cutting edge researches in: 1) theoretically machine learning, 2) stochastic algorithms, 3) generative models, 4) adversarial learning, 5) explainable computer vision.

The Multiplicative Noise in Stochastic Gradient Descent: Data-Dependent Regularization, Continuous and Discrete Approximation
Jingfeng Wu, Wenqing Hu, Haoyi Xiong, Jun Huan, Zhanxing Zhu
submitted
bibtex / arXiv

We re-interpret the noise in SGD from the perspective of random sampling, and obtain several novel results in regard to understand the regularization and approximation of SGD.

Tangent-Normal Adversarial Regularization for Semi-supervised Learning
Bing Yu^*, Jingfeng Wu^*, Jinwen Ma, Zhanxing Zhu
Conference on Computer Vision and Pattern Recognition (CVPR), 2019, oral
bibtex / arXiv / slides / poster

We present a novel manifold regularization method for semi-supervised learning, which is realized via adversarial training.

The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Minima and Regularization Effects
Zhanxing Zhu^*, Jingfeng Wu^*, Bing Yu, Lei Wu, Jinwen Ma
International Conference on Machine Learning (ICML), 2019
bibtex / arXiv / slides / poster

We study the noise structure of stochastic gradient descent, and demonstrate its benefits on helping the dynamic escaping from sharp minima.

• Research Assistant at Johns Hopkins University, Aug. 2019 - Now

• Research Intern at Baidu Big Data Lab, Dec. 2018 - May 2019

• Research Assistant at Peking University, July 2017 - Dec. 2018

• Empirical Fisher Approximation.
• Adversarial Regularization.
• Intrinsic Gradient Descent.

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