Academics

Methods and Theory on Model Selection and Model Averaging

Time:9:50-12:15,Sept.15,Sept.22,Sept.29,Oct.9,2022

Venue:Conference Room 1,Jin Chun Yuan West Bldg.;Zoom Meeting ID: 271 534 5558 Passcode: YMSC

Speaker:Prof.Yuhong Yang(University of Minnesota)

Description

Model selection and its diagnosis are foundational elements in modern statistical and machine learning applications that serve the purpose of obtaining reliable information and reproducible results. In this short course, we introduce the principles and theories on model selection and model averaging and their applications in high-dimensional regression. Model selection methods include information criteria (AIC, BIC etc), cross validation, penalized regression (LASSO, SCAD, MCP) and more. We will learn to understand their differences, connections, performances, limitations, proper uses, and approaches to achieving the best performance without knowing which method is the best for the data at hand. In addition, we will study new tools to characterize model selection reliability. When model selection uncertainty is high, model averaging/combining typically offers more accurate prediction and more reliable conclusions. Theoretical results covered include model selection consistency, consistent cross validation, adaptive minimax optimal regression learning in high-dimensional regression, and optimalities of model averaging methods.

DATEAugust 30, 2022
SHARE
Related News
    • 0

      Geometric Numerical Methods for Dynamical Systems II

      Record: YesLevel: GraduateLanguage: ChinesePrerequisiteAbstractThis course is a continuation of last semester and will cover the following topics:1) Normal forms of Hamiltonian systems and bifurcation theory;2) Averaging methods of classical perturbation theory;3) KAM stability of Hamiltonian systems;4) Effective stability of nearly integrable systems;5) Numerical stability of symplectic g...

    • 1

      The Application of Machine Learning Methods to the Solution of Partial Differential Equations II

      Record: NoLevel: UndergraduateLanguage: ChinesePrerequisiteBasic knowledge on numerical methods for partial differential equations and machine learning methodsAbstractThis course reviews the publications of the recent years on using machine learning methods to solve partial differential equations, such as Physics Informed Neural Network (PINN). The course will cover the materials on forward me...