---
title: "Efficient Bayesian Task-Level Transfer Learning"
authors:
  - "Daniel M. Roy"
  - "Leslie Pack Kaelbling"
doi: ""
url: "https://ijcai.org/proceedings/2007/418"
pdf: "https://ijcai.org/proceedings/2007/0418.pdf"
venue: "Proceedings of the Twentieth International Joint Conference on Artificial Intelligence"
year: 2007
pages: "2599-2603"
track: ""
keywords:
---

# Efficient Bayesian Task-Level Transfer Learning

Daniel M. Roy, Leslie Pack Kaelbling

*Proceedings of the Twentieth International Joint Conference on Artificial Intelligence*, pages 2599-2603.

## Abstract

In this paper, we show how using the Dirichlet Process mixture model as a generative model of data sets provides a simple and effective method for transfer learning. In particular, we present a hierarchical extension of the classic Naive Bayes classifier that couples multiple Naive Bayes classifiers by placing a Dirichlet Process prior over their parameters and show how recent advances in approximate inference in the Dirichlet Process mixture model enable efficient inference. We evaluate the resulting model in a meeting domain, in which the system decides, based on a learned model of the user's behavior, whether to accept or reject the request on his or her behalf. The extended model outperforms the standard Naive Bayes model by using data from other users to influence its predictions.