Group sparse optimization for learning predictive state representations

DOI10.1016/j.ins.2017.05.023zbMath1435.68288OpenAlexW2614842106MaRDI QIDQ778374

Bilian Chen, Biyang Ma, Mengda He, Jing Tang, Yi-Feng Zeng

Publication date: 2 July 2020

Published in: Information Sciences (Search for Journal in Brave)

Full work available at URL: http://nrl.northumbria.ac.uk/id/eprint/44593/1/R4_Information_Sciences_Group_Sparse_Optimization_for_Learning_Predictive_State_Representations.pdf

zbMATH Keywords

alternating direction method of multipliersgroup sparse optimizationpredictive state representations

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Semidefinite programming (90C22) Learning and adaptive systems in artificial intelligence (68T05) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20) Agent technology and artificial intelligence (68T42)

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Cites Work

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• Planning and acting in partially observable stochastic domains
• Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
• Enhancing sparsity by reweighted \(\ell _{1}\) minimization
• Near-ideal model selection by \(\ell _{1}\) minimization
• Efficient block-coordinate descent algorithms for the group Lasso
• Analyzing Weighted $\ell_1$ Minimization for Sparse Recovery With Nonuniform Sparse Models
• Model Selection and Estimation in Regression with Grouped Variables

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