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Research Interests:
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Biography
I received my Ph.D. in Applied Mathematics from Zhejiang University in 2013. Before joining at the Laboratory for Information and Inference Systems in the École Polytechnique Fédérale de Lausanne (EPFL) in June 2017, I was a postdoc at the City University of Hong Kong and the Italian Institue of Technology.
Publications with LIONS (most recent)
Kernel Conjugate Gradient Methods with Random Projections
Applied and Computational Harmonic Analysis. 2021. Vol. 55, p. 223-269. DOI : 10.1016/j.acha.2021.05.004.Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
Journal of Machine Learning Research. 2020. Vol. 21, num. 147, p. 1-63.Convergences of Regularized Algorithms and Stochastic Gradient Methods with Random Projections
Journal of Machine Learning Research. 2020. Vol. 21, num. 20, p. 1-44.Optimal Rates for Spectral Algorithms with Least-Squares Regression over Hilbert Spaces
Applied and Computational Harmonic Analysis. 2020. Vol. 48, num. 3, p. 868-890. DOI : 10.1016/j.acha.2018.09.009.Chemical machine learning with kernels: The impact of loss functions
International Journal Of Quantum Chemistry. 2019-05-05. Vol. 119, num. 9, p. e25872. DOI : 10.1002/qua.25872.A Learning-Based Framework for Quantized Compressed Sensing
IEEE Signal Processing Letters. 2019. Vol. 26, num. 6, p. 883-887. DOI : 10.1109/LSP.2019.2898350.Optimal Convergence for Distributed Learning with Stochastic Gradient Methods and Spectral Algorithms
2018-09-03
Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods
2018-06-08. 35th International Conference on Machine Learning, Stockholm, Sweden, July 10 -15, 2018.Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces
2018-03-11. 35th International Conference on Machine Learning (ICML), Stockholm, Sweden, July 10-15, 2018.Chemical machine learning with kernels: The key impact of loss functions
2018