Decision-Theoretic Sparsification for Gaussian Process Preference Learning

M. Ehsan Abbasnejad; Edwin V. Bonilla; Scott Sanner

doi:10.1007/978-3-642-40991-2_33

ScienceGate Book Chapters

BOOK-CHAPTER

Decision-Theoretic Sparsification for Gaussian Process Preference Learning

M. Ehsan Abbasnejad Edwin V. Bonilla Scott Sanner

Year: 2013 Lecture notes in computer science Pages: 515-530 Publisher: Springer Science+Business Media

DOI: 10.1007/978-3-642-40991-2_33

Get Full-Text PDF Get Analytical Report

Keywords:

Computer science Preference Upper and lower bounds Artificial intelligence Machine learning Preference learning Entropy (arrow of time) Gaussian process Gaussian Function (biology) Mathematical optimization Algorithm Mathematics Statistics

Metrics

Cited By

0.00

FWCI (Field Weighted Citation Impact)

Refs

0.02

Citation Normalized Percentile

Is in top 1%

Is in top 10%

Topics

Advanced Bandit Algorithms Research

Social Sciences → Decision Sciences → Management Science and Operations Research

Gaussian Processes and Bayesian Inference

Physical Sciences → Computer Science → Artificial Intelligence

Advanced Multi-Objective Optimization Algorithms

Physical Sciences → Computer Science → Computational Theory and Mathematics

Decision-Theoretic Sparsification for Gaussian Process Preference Learning

Metrics

Topics

Related Documents

Efficient sparsification for Gaussian process regression

Active Preference-Based Gaussian Process Regression for Reward Learning

Investigation of Facial Preference Using Gaussian Process Preference Learning and Generative Image Model

Predicting the Humorousness of Tweets Using Gaussian Process Preference Learning

Predicting Humorousness and Metaphor Novelty with Gaussian Process Preference Learning