Paper Info
Title
Machine Teaching for Bayesian Learners in the Exponential Family.
Abstract
What if there is a teacher who knows the learning goal and wants to design good training data for a machine learner? We propose an optimal teaching framework aimed at learners who employ Bayesian models. Our framework is expressed as an optimization problem over teaching examples that balance the future loss of the learner and the effort of the teacher. This optimization problem is in general hard. In the case where the learner employs conjugate exponential family models, we present an approximate algorithm for finding the optimal teaching set. Our algorithm optimizes the aggregate sufficient statistics, then unpacks them into actual teaching examples. We give several examples to illustrate our framework.
Year
Venue
Field
2013
NIPS
Training set,Computer science,Exponential family,Artificial intelligence,Sufficient statistic,Optimization problem,Machine learning,Bayesian probability
DocType
Citations 
PageRank 
Conference
21
1.14
References 
Authors
11
1
Name
Order
Citations
PageRank
Xiaojin Zhu13586222.74