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Most Influential Subset Selection: Challenges, Promises, and Beyond
Yuzheng Hu
, Pingbang Hu
,
Han Zhao
,
Jiaqi W. Ma
School of Information Sciences
Siebel School of Computing and Data Science
National Center for Supercomputing Applications (NCSA)
Research output
:
Contribution to journal
›
Conference article
›
peer-review
Overview
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Dive into the research topics of 'Most Influential Subset Selection: Challenges, Promises, and Beyond'. Together they form a unique fingerprint.
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Keyphrases
Subset Selection
100%
Collective Influence
100%
Influence Function
66%
Subset Selection Problem
33%
Computational Efficiency
33%
Nonlinear Neural Networks
33%
Linear Regression
33%
Failure Mode
33%
Training Data
33%
Performance Efficiency
33%
Greedy Heuristic
33%
Training Samples
33%
Adaptivity
33%
Additive Structure
33%
Classification Task
33%
Machine Learning Models
33%
Dominant Classes
33%
Additive Metric
33%
Complex Scenarios
33%
Computer Science
Feature Subset Selection
100%
Influence Function
50%
Computational Efficiency
25%
Neural Network
25%
Training Data
25%
Training Sample
25%
Classification Task
25%
Greedy Heuristic
25%
Dominant Class
25%
Machine Learning Model
25%