A Probabilistic Framework for Zero-Shot Multi-Label Learning

Abhilash Gaure, Aishwarya Gupta, Vinay Kumar Verma, Piyush Rai
Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, PMLR R15:31-40, 2017.

Abstract

We present a probabilistic framework for multi-label learning for the setting when the test data may require predicting labels that were not available at training time (i.e., the zero-shot learning setting). We de- velop a probabilistic model that leverages the co-occurrence statistics of the labels via a joint generative model for the label matrix (which denotes the label presence/absence for each training example) and for the label co- occurrence matrix (which denotes how many times a pair of labels co-occurs with each other). In addition to handling the unseen la- bels at test time, leveraging the co-occurrence information may also help in the standard multi-label learning setting, especially if the number of training examples is very small and/or the label matrix of training examples has a large fraction of missing entries. Our ex- perimental results demonstrate the efficacy of our model in handling unseen labels.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR15-gaure17a, title = {A Probabilistic Framework for Zero-Shot Multi-Label Learning}, author = {Gaure, Abhilash and Gupta, Aishwarya and Verma, Vinay Kumar and Rai, Piyush}, booktitle = {Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence}, pages = {31--40}, year = {2017}, editor = {Elidan, Gal and Kersting, Kristian}, volume = {R15}, series = {Proceedings of Machine Learning Research}, month = {11--15 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r15/main/assets/gaure17a/gaure17a.pdf}, url = {https://proceedings.mlr.press/r15/gaure17a.html}, abstract = {We present a probabilistic framework for multi-label learning for the setting when the test data may require predicting labels that were not available at training time (i.e., the zero-shot learning setting). We de- velop a probabilistic model that leverages the co-occurrence statistics of the labels via a joint generative model for the label matrix (which denotes the label presence/absence for each training example) and for the label co- occurrence matrix (which denotes how many times a pair of labels co-occurs with each other). In addition to handling the unseen la- bels at test time, leveraging the co-occurrence information may also help in the standard multi-label learning setting, especially if the number of training examples is very small and/or the label matrix of training examples has a large fraction of missing entries. Our ex- perimental results demonstrate the efficacy of our model in handling unseen labels.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Probabilistic Framework for Zero-Shot Multi-Label Learning %A Abhilash Gaure %A Aishwarya Gupta %A Vinay Kumar Verma %A Piyush Rai %B Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2017 %E Gal Elidan %E Kristian Kersting %F pmlr-vR15-gaure17a %I PMLR %P 31--40 %U https://proceedings.mlr.press/r15/gaure17a.html %V R15 %X We present a probabilistic framework for multi-label learning for the setting when the test data may require predicting labels that were not available at training time (i.e., the zero-shot learning setting). We de- velop a probabilistic model that leverages the co-occurrence statistics of the labels via a joint generative model for the label matrix (which denotes the label presence/absence for each training example) and for the label co- occurrence matrix (which denotes how many times a pair of labels co-occurs with each other). In addition to handling the unseen la- bels at test time, leveraging the co-occurrence information may also help in the standard multi-label learning setting, especially if the number of training examples is very small and/or the label matrix of training examples has a large fraction of missing entries. Our ex- perimental results demonstrate the efficacy of our model in handling unseen labels. %Z Reissued by PMLR on 04 October 2026.
APA
Gaure, A., Gupta, A., Verma, V.K. & Rai, P.. (2017). A Probabilistic Framework for Zero-Shot Multi-Label Learning. Proceedings of the 33rd Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R15:31-40 Available from https://proceedings.mlr.press/r15/gaure17a.html. Reissued by PMLR on 04 October 2026.

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