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I am a first year Ph.D. student at Johns Hopkins University, Computer Science Department. I am supervised by 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 fortunate to work with Prof. Jinwen Ma and Prof. Zhanxing Zhu. Email / CV / Google Scholar / Github |
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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. |
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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. |
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We present a novel manifold regularization method for semi-supervised learning, which is realized via adversarial training. |
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We study the noise structure of stochastic gradient descent, and demonstrate its benefits on helping the dynamic escaping from sharp minima. |
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