Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
Person
|
| gptkbp:affiliation |
gptkb:Microsoft_Research
|
| gptkbp:collaboratedWith |
gptkb:Shalmali_Joshi
gptkb:Emma_Pierson Lihong Li |
| gptkbp:contribution |
gptkb:Vowpal_Wabbit
|
| gptkbp:education |
gptkb:University_of_California,_Berkeley
|
| gptkbp:field |
Statistics
|
| gptkbp:hasAwards |
Best Paper Award
Innovations in Machine Learning Award |
| gptkbp:hasPublications |
gptkb:Machine_Learning_for_Social_Good
Learning to Rank Contextual Bandits Approximate Dynamic Programming Learning from Demonstration A Survey of Reinforcement Learning Statistical Methods for Machine Learning Applications of Reinforcement Learning Reinforcement Learning for Robotics Advances in Reinforcement Learning Exploration in Reinforcement Learning A Theory of Online Learning Challenges in Machine Learning Large Scale Learning Learning to Act Using Reinforcement Learning Multi-armed Bandits Online Convex Optimization Statistical Learning with Applications Theoretical Analysis of Online Learning The_Future_of_Machine_Learning Machine_Learning_for_Computer_Vision Machine_Learning_for_Climate_Change The_Role_of_Exploration_in_Reinforcement_Learning Machine_Learning_in_Practice Machine_Learning_for_Healthcare Machine_Learning_for_Finance Machine_Learning_for_Data_Science Learning_with_Kernels Machine_Learning_for_Natural_Language_Processing The_Importance_of_Exploration_in_Learning Theoretical_Foundations_of_Reinforcement_Learning |
| https://www.w3.org/2000/01/rdf-schema#label |
John Langford
|
| gptkbp:influenced |
Many Researchers in AI
|
| gptkbp:influencedBy |
gptkb:David_Cohn
|
| gptkbp:knownFor |
Machine Learning
|
| gptkbp:nationality |
American
|
| gptkbp:profession |
Computer_Scientist
|
| gptkbp:publishes |
Reinforcement Learning
Online Learning Statistical Learning Theory |
| gptkbp:researchAreas |
Artificial Intelligence
|