Skip to main navigation
Skip to search
Skip to main content
Tel Aviv University Home
Update Request & User Guide (TAU staff only)
Home
Experts
Research units
Research output
Datasets
Prizes
Activities
Courses
Press/Media
Search by expertise, name or affiliation
Bayesian Design Principles for Frequentist Sequential Learning
Yunbei Xu
*
,
Assaf Zeevi
*
Corresponding author for this work
Columbia University
Research output
:
Contribution to journal
›
Conference article
›
peer-review
2
Scopus citations
Overview
Fingerprint
Fingerprint
Dive into the research topics of 'Bayesian Design Principles for Frequentist Sequential Learning'. Together they form a unique fingerprint.
Sort by
Weight
Alphabetically
Keyphrases
Design Principles
100%
Bandit Learning
100%
Sequential Learning
100%
Frequentist
100%
Bayesian Design
100%
Stochastic Environment
50%
Regret
50%
Learning Problems
50%
Novel Algorithm
50%
Making Decisions
50%
Optimization Approach
50%
Multi-arm Bandit
50%
Reinforcement Learning
50%
Empirical Performance
50%
Bayesian Probability
50%
Adversarial Setting
50%
Reinforcement Learning Algorithm
50%
Adversarial Environment
50%
Prior-free
50%
Bayesian Principle
50%
Non-stationary Environments
50%
Linear Bandits
50%
Computer Science
Sequential Learning
100%
Reinforcement Learning
100%
Bayesian Design
100%
Learning Problem
50%
Adversarial Setting
50%
Major Application
50%
Engineering
Reinforcement Learning
100%
Illustrates
50%
Optimization Approach
50%
Major Application
50%
Chemical Engineering
Reinforcement Learning
100%