Instance Label Prediction by Dirichlet Process Multiple Instance Learning

Melih Kandemir Heidelberg University HCI/IWR, Fred Hamprecht Heidelberg University HCI/IWR
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:294-303, 2014.

Abstract

We propose a generative Bayesian model that predicts instance labels from weak (bag-level) supervision. We solve this problem by simulta- neously modeling class distributions by Gaussian mixture models and inferring the class labels of positive bag instances that satisfy the multiple in- stance constraints. We employ Dirichlet process priors on mixture weights to automate model se- lection, and efficiently infer model parameters and positive bag instances by a constrained varia- tional Bayes procedure. Our method improves on the state-of-the-art of instance classification from weak supervision on 20 benchmark text catego- rization data sets and one histopathology cancer diagnosis data set.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-hci-iwr14a, title = {Instance Label Prediction by {D}irichlet Process Multiple Instance Learning}, author = {HCI/IWR, Melih Kandemir Heidelberg University and HCI/IWR, Fred Hamprecht Heidelberg University}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {294--303}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/hci-iwr14a/hci-iwr14a.pdf}, url = {https://proceedings.mlr.press/r12/hci-iwr14a.html}, abstract = {We propose a generative Bayesian model that predicts instance labels from weak (bag-level) supervision. We solve this problem by simulta- neously modeling class distributions by Gaussian mixture models and inferring the class labels of positive bag instances that satisfy the multiple in- stance constraints. We employ Dirichlet process priors on mixture weights to automate model se- lection, and efficiently infer model parameters and positive bag instances by a constrained varia- tional Bayes procedure. Our method improves on the state-of-the-art of instance classification from weak supervision on 20 benchmark text catego- rization data sets and one histopathology cancer diagnosis data set.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Instance Label Prediction by Dirichlet Process Multiple Instance Learning %A Melih Kandemir Heidelberg University HCI/IWR %A Fred Hamprecht Heidelberg University HCI/IWR %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-hci-iwr14a %I PMLR %P 294--303 %U https://proceedings.mlr.press/r12/hci-iwr14a.html %V R12 %X We propose a generative Bayesian model that predicts instance labels from weak (bag-level) supervision. We solve this problem by simulta- neously modeling class distributions by Gaussian mixture models and inferring the class labels of positive bag instances that satisfy the multiple in- stance constraints. We employ Dirichlet process priors on mixture weights to automate model se- lection, and efficiently infer model parameters and positive bag instances by a constrained varia- tional Bayes procedure. Our method improves on the state-of-the-art of instance classification from weak supervision on 20 benchmark text catego- rization data sets and one histopathology cancer diagnosis data set. %Z Reissued by PMLR on 04 October 2026.
APA
HCI/IWR, M.K.H.U. & HCI/IWR, F.H.H.U.. (2014). Instance Label Prediction by Dirichlet Process Multiple Instance Learning. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:294-303 Available from https://proceedings.mlr.press/r12/hci-iwr14a.html. Reissued by PMLR on 04 October 2026.

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