Mathematical Theories of Machine Learning - Theory and Applications
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Published By Springer International Publishing

9783030170752, 9783030170769

Latest Documents Most Cited Documents Contributed Authors Related Sources Related Keywords

Improved Sample Complexity in Sparse Subspace Clustering with Noisy and Missing Observations

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_9 ◽

2019 ◽

pp. 89-101

Author(s):

Bin Shi ◽

S. S. Iyengar

Keyword(s):

Subspace Clustering ◽

Missing Observations ◽

Sample Complexity ◽

Sparse Subspace Clustering

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Related Work on Geometry of Non-Convex Programs

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_6 ◽

2019 ◽

pp. 39-44

Author(s):

Bin Shi ◽

S. S. Iyengar

Keyword(s):

Convex Programs

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General Framework of Mathematics

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_2 ◽

2019 ◽

pp. 13-16

Author(s):

Bin Shi ◽

S. S. Iyengar

Keyword(s):

General Framework

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Necessary Notations of the Proposed Method

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_5 ◽

2019 ◽

pp. 35-37

Author(s):

Bin Shi ◽

S. S. Iyengar

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Development of Novel Techniques of CoCoSSC Method

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_4 ◽

2019 ◽

pp. 29-33

Author(s):

Bin Shi ◽

S. S. Iyengar

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A Conservation Law Method Based on Optimization

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_8 ◽

2019 ◽

pp. 63-85

Author(s):

Bin Shi ◽

S. S. Iyengar

Keyword(s):

Conservation Law

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Online Discovery for Stable and Grouping Causalities in Multivariate Time Series

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_10 ◽

2019 ◽

pp. 103-119

Author(s):

Bin Shi ◽

S. S. Iyengar

Keyword(s):

Time Series ◽

Multivariate Time Series

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Gradient Descent Converges to Minimizers: Optimal and Adaptive Step-Size Rules

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_7 ◽

2019 ◽

pp. 47-62

Author(s):

Bin Shi ◽

S. S. Iyengar

Keyword(s):

Gradient Descent ◽

Step Size ◽

Adaptive Step Size ◽

Adaptive Step

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Introduction

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_1 ◽

2019 ◽

pp. 3-11

Author(s):

Bin Shi ◽

S. S. Iyengar

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Optimization Formulation

Mathematical Theories of Machine Learning - Theory and Applications ◽

10.1007/978-3-030-17076-9_3 ◽

2019 ◽

pp. 17-28

Author(s):

Bin Shi ◽

S. S. Iyengar

Keyword(s):

Optimization Formulation

Download Full-text

Mathematical Theories of Machine Learning - Theory and ApplicationsLatest Publications

TOTAL DOCUMENTS

H-INDEX

Published By Springer International Publishing

Improved Sample Complexity in Sparse Subspace Clustering with Noisy and Missing Observations

Related Work on Geometry of Non-Convex Programs

General Framework of Mathematics

Necessary Notations of the Proposed Method

Development of Novel Techniques of CoCoSSC Method

A Conservation Law Method Based on Optimization

Online Discovery for Stable and Grouping Causalities in Multivariate Time Series

Gradient Descent Converges to Minimizers: Optimal and Adaptive Step-Size Rules

Introduction

Optimization Formulation

Mathematical Theories of Machine Learning - Theory and Applications
Latest Publications