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Learning and inference in the presence of corrupted inputs
Uriel Feige
,
Yishay Mansour
, Robert E. Schapire
School of Computer Science and AI
Weizmann Institute of Science
Princeton University
Research output
:
Contribution to journal
›
Conference article
›
peer-review
9
Scopus citations
Overview
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Keyphrases
Adversary
100%
Bipartite Graph
100%
Oracle
66%
Minimum Vertex Cover
66%
Near-optimal
66%
Expected Error
66%
Learning Algorithm
33%
Optimal Algorithm
33%
Corrupt
33%
Inference Problem
33%
Zero-sum Game
33%
Unseen
33%
Efficient Learning
33%
Local Algorithms
33%
Target Function
33%
Classification Problem
33%
Generalization Bounds
33%
Settings-based
33%
Adversarial Setting
33%
Maximum Matching
33%
Sample Return
33%
Prediction Function
33%
Optimal Error Rate
33%
Computer Science
Bipartite Graph
100%
Learning Algorithm
33%
Efficient Algorithm
33%
Local Algorithm
33%
Optimal Algorithm
33%
Achievable Rate
33%
Adversarial Setting
33%
Prediction Function
33%
Target Function
33%
Maximum Matching
33%
Mathematics
Bipartite Graph
100%
Error Rate
33%
Function Prediction
33%