Domain Knowledge Uncertainty and Probabilistic Parameter Constraints

Yi Mao, Guy Lebanon
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:383-390, 2009.

Abstract

Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter space. In contrast to hard parameter constraints, our approach is effective also when the domain knowledge is inaccurate and generally results in superior modeling accuracy. We focus on generative and conditional modeling where the parameters are assigned a Dirichlet or Gaussian prior and demonstrate the framework with experiments on both synthetic and real-world data.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-mao09a, title = {Domain Knowledge Uncertainty and Probabilistic Parameter Constraints}, author = {Mao, Yi and Lebanon, Guy}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {383--390}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/mao09a/mao09a.pdf}, url = {https://proceedings.mlr.press/r7/mao09a.html}, abstract = {Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter space. In contrast to hard parameter constraints, our approach is effective also when the domain knowledge is inaccurate and generally results in superior modeling accuracy. We focus on generative and conditional modeling where the parameters are assigned a Dirichlet or Gaussian prior and demonstrate the framework with experiments on both synthetic and real-world data.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Domain Knowledge Uncertainty and Probabilistic Parameter Constraints %A Yi Mao %A Guy Lebanon %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-mao09a %I PMLR %P 383--390 %U https://proceedings.mlr.press/r7/mao09a.html %V R7 %X Incorporating domain knowledge into the modeling process is an effective way to improve learning accuracy. However, as it is provided by humans, domain knowledge can only be specified with some degree of uncertainty. We propose to explicitly model such uncertainty through probabilistic constraints over the parameter space. In contrast to hard parameter constraints, our approach is effective also when the domain knowledge is inaccurate and generally results in superior modeling accuracy. We focus on generative and conditional modeling where the parameters are assigned a Dirichlet or Gaussian prior and demonstrate the framework with experiments on both synthetic and real-world data. %Z Reissued by PMLR on 04 October 2026.
APA
Mao, Y. & Lebanon, G.. (2009). Domain Knowledge Uncertainty and Probabilistic Parameter Constraints. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:383-390 Available from https://proceedings.mlr.press/r7/mao09a.html. Reissued by PMLR on 04 October 2026.

Related Material