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Tilted Sparse Additive Models
Yingjie Wang · Hong Chen · Weifeng Liu · Fengxiang He · Tieliang Gong · YouCheng Fu · Dacheng Tao

Thu Jul 27 06:32 PM -- 06:40 PM (PDT) @ Meeting Room 313

Additive models have been burgeoning in data analysis due to their flexible representation and desirable interpretability. However, most existing approaches are constructed under empirical risk minimization (ERM), and thus perform poorly in situations where average performance is not a suitable criterion for the problems of interest, e.g., data with complex non-Gaussian noise, imbalanced labels or both of them. In this paper, a novel class of sparse additive models is proposed under tilted empirical risk minimization (TERM), which addresses the deficiencies in ERM by imposing tilted impact on individual losses, and is flexibly capable of achieving a variety of learning objectives, e.g., variable selection, robust estimation, imbalanced classification and multiobjective learning. On the theoretical side, a learning theory analysis which is centered around the generalization bound and function approximation error bound (under some specific data distributions) is conducted rigorously. On the practical side, an accelerated optimization algorithm is designed by integrating Prox-SVRG and random Fourier acceleration technique. The empirical assessments verify the competitive performance of our approach on both synthetic and real data.

Author Information

Yingjie Wang (China University of Petroleum)
Hong Chen (Huazhong Agricultural University)
Weifeng Liu (China University of Petroleum (East China))
Fengxiang He (University of Edinburgh)

Fengxiang He is a Lecturer at Artificial Intelligence and its Applications Institute, School of Informatics, University of Edinburgh. He received his BSc in statistics from University of Science and Technology of China, MPhil and PhD in computer science from University of Sydney. He was an Algorithm Scientist at JD Explore Academy, JD.com, Inc., leading its trustworthy AI team. His research interest is in the theory and practice of trustworthy AI, including deep learning theory, privacy-preserving machine learning, algorithmic game theory, etc., as well as applications in finance and economics. He is an Area Chair of UAI, AISTATS, and ACML.

Tieliang Gong (Xi'an Jiaotong University)
YouCheng Fu (HuaZhong Agriculture University)
Dacheng Tao

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