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High-Dimensional Data Analysis Using Greedy Algorithms

来源: 10-16

时间:Thur.,13:00-16:00 Oct. 17 / 24, 2024

地点:Jingzhai 105

主讲人:Ching-Kang Ing

Speaker:

Ching-Kang Ing (NTHU)

Time:

Thur.,13:00-16:00

Oct. 17 / 24, 2024

Venue:

Jingzhai 105

Description:

Greedy algorithms, particularly the orthogonal greedy algorithm (OGA), are frequently used for high-dimensional model selection as an alternative to Lasso. In this course, I will discuss the statistical properties of OGA when applied in conjunction with high-dimensional criteria for model selection in both stationary and non-stationary high-dimensional time series models. Additionally, I will explain how greedy-type algorithms can be employed to estimate high-dimensional sparse covariance matrices of stationary time series. Furthermore, I will demonstrate modifications of OGA for high-dimensional model selection in the presence of covariate shift. If time permits, I will introduce the performance of the Chebyshev Greedy Algorithm (CGA), a non-linear counterpart of OGA, in certain high-dimensional non-linear models.

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