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Algorithms with logarithmic or sublinear regret for constrained contextual bandits
Huasen Wu
,
R. Srikant
, Xin Liu
, Chong Jiang
Electrical and Computer Engineering
Coordinated Science Lab
Office of the Vice Chancellor for Research and Innovation
Siebel School of Computing and Data Science
Research output
:
Contribution to journal
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Conference article
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peer-review
Overview
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Dive into the research topics of 'Algorithms with logarithmic or sublinear regret for constrained contextual bandits'. Together they form a unique fingerprint.
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Keyphrases
Sub-linear Regret
100%
Contextual Bandits
100%
Logarithmic Regret
100%
Expected Payoff
60%
Time Constraints
40%
Budget Constraint
40%
Linear Programming
40%
Upper Confidence Bound
40%
Context Distribution
40%
Computationally Efficient Algorithms
20%
Algorithm Design
20%
Distribution Cost
20%
Boundary Cases
20%
Oracle
20%
Costing System
20%
General Systems
20%
Unit Cost
20%
Binding Method
20%
Near-optimality
20%
Linear Algorithm
20%
Complex Coupling
20%
Regret Bounds
20%
Heterogeneous Costs
20%
Computer Science
Linear Programming
100%
Time Constraint
66%
Budget Constraint
66%
Confidence Bound
66%
Approximation (Algorithm)
33%
Efficient Algorithm
33%
Algorithm Design
33%