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Convex-constrained Sparse Additive Modeling and Its Extensions

Convex-constrained Sparse Additive Modeling and Its Extensions

1 May 2017
Junming Yin
Yaoliang Yu
ArXiv (abs)PDFHTML

Papers citing "Convex-constrained Sparse Additive Modeling and Its Extensions"

2 / 2 papers shown
Title
Interpretable Machine Learning: Fundamental Principles and 10 Grand
  Challenges
Interpretable Machine Learning: Fundamental Principles and 10 Grand Challenges
Cynthia Rudin
Chaofan Chen
Zhi Chen
Haiyang Huang
Lesia Semenova
Chudi Zhong
FaMLAI4CELRM
240
677
0
20 Mar 2021
Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian
  Reparameterization offers Significant Performance and Efficiency Gains
Optimizing Nondecomposable Data Dependent Regularizers via Lagrangian Reparameterization offers Significant Performance and Efficiency Gains
Sathya Ravi
Abhay Venkatesh
G. Fung
Vikas Singh
41
3
0
26 Sep 2019
1