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 frame-work 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 con-jugate 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.
This thesis proposes, demonstrates, and evaluates, the concept of the normative Intelligent Tutorin...
We propose and demonstrate a methodology for building tractable normative intelligent tutoring syste...
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic a...
We use Bayesian optimization to learn curricula for word representation learning, optimizing perform...
Recent work on intelligent tutoring systems has used Bayesian networks to model students ’ acquisiti...
I draw the reader’s attention to machine teaching, the prob-lem of finding an optimal training set g...
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore,...
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deli...
A general mathematical framework is developed for learning algorithms. A learning task belongs to ei...
A general approach to Bayesian learning revisits some classical results, which study which functiona...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
The use of machine learning algorithms frequently involves careful tuning of learning parameters and...
This paper explores the why and what of statistical learning from a computational modelling perspect...
The use of machine learning algorithms frequently involves careful tuning of learning parameters and...
Abstract Adaptive learning games should provide opportunities for the student to learn as well as mo...
This thesis proposes, demonstrates, and evaluates, the concept of the normative Intelligent Tutorin...
We propose and demonstrate a methodology for building tractable normative intelligent tutoring syste...
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic a...
We use Bayesian optimization to learn curricula for word representation learning, optimizing perform...
Recent work on intelligent tutoring systems has used Bayesian networks to model students ’ acquisiti...
I draw the reader’s attention to machine teaching, the prob-lem of finding an optimal training set g...
Intelligent tutoring systems adapt the curriculum to the needs of the individual student. Therefore,...
We consider the machine teaching problem in a classroom-like setting wherein the teacher has to deli...
A general mathematical framework is developed for learning algorithms. A learning task belongs to ei...
A general approach to Bayesian learning revisits some classical results, which study which functiona...
Bayesian optimization has recently emerged in the machine learning community as a very effective aut...
The use of machine learning algorithms frequently involves careful tuning of learning parameters and...
This paper explores the why and what of statistical learning from a computational modelling perspect...
The use of machine learning algorithms frequently involves careful tuning of learning parameters and...
Abstract Adaptive learning games should provide opportunities for the student to learn as well as mo...
This thesis proposes, demonstrates, and evaluates, the concept of the normative Intelligent Tutorin...
We propose and demonstrate a methodology for building tractable normative intelligent tutoring syste...
This tutorial text gives a unifying perspective on machine learning by covering both probabilistic a...
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