Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data

Martin Schiegg, Marion Neumann, Kristian Kersting
Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, PMLR 22:1002-1011, 2012.

Abstract

We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP facilitates novel, interesting tasks such as regression based on logical constraints or drawing probabilistic logical conclusions about regression data, thus putting “machines reading regression data” in reach.

Cite this Paper

BibTeX
@InProceedings{pmlr-v22-schiegg12, title = {Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data}, author = {Schiegg, Martin and Neumann, Marion and Kersting, Kristian}, booktitle = {Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics}, pages = {1002--1011}, year = {2012}, editor = {Lawrence, Neil D. and Girolami, Mark}, volume = {22}, series = {Proceedings of Machine Learning Research}, address = {La Palma, Canary Islands}, month = {21--23 Apr}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v22/schiegg12/schiegg12.pdf}, url = {https://proceedings.mlr.press/v22/schiegg12.html}, abstract = {We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP facilitates novel, interesting tasks such as regression based on logical constraints or drawing probabilistic logical conclusions about regression data, thus putting “machines reading regression data” in reach.} }
Endnote
%0 Conference Paper %T Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data %A Martin Schiegg %A Marion Neumann %A Kristian Kersting %B Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2012 %E Neil D. Lawrence %E Mark Girolami %F pmlr-v22-schiegg12 %I PMLR %P 1002--1011 %U https://proceedings.mlr.press/v22/schiegg12.html %V 22 %X We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP facilitates novel, interesting tasks such as regression based on logical constraints or drawing probabilistic logical conclusions about regression data, thus putting “machines reading regression data” in reach.
RIS
TY - CPAPER TI - Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data AU - Martin Schiegg AU - Marion Neumann AU - Kristian Kersting BT - Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics DA - 2012/03/21 ED - Neil D. Lawrence ED - Mark Girolami ID - pmlr-v22-schiegg12 PB - PMLR DP - Proceedings of Machine Learning Research VL - 22 SP - 1002 EP - 1011 L1 - http://proceedings.mlr.press/v22/schiegg12/schiegg12.pdf UR - https://proceedings.mlr.press/v22/schiegg12.html AB - We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In this way, the resulting mixed graphical model, called Markov logic mixtures of Gaussian processes (MLxGP), solves joint Bayesian non-parametric regression and probabilistic relational inference tasks. In turn, MLxGP facilitates novel, interesting tasks such as regression based on logical constraints or drawing probabilistic logical conclusions about regression data, thus putting “machines reading regression data” in reach. ER -
APA
Schiegg, M., Neumann, M. & Kersting, K.. (2012). Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data. Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 22:1002-1011 Available from https://proceedings.mlr.press/v22/schiegg12.html.

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