Learning from Point Sets with Observational Bias

Liang Xiong Carnegie Mellon University, Jeff Schneider Carnegie Mellon University
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:37-45, 2014.

Abstract

Many objects can be represented as sets of multi- dimensional points. A common approach to learning from these point sets is to assume that each set is an i.i.d. sample from an unknown un- derlying distribution, and then estimate the sim- ilarities between these distributions. In realistic situations, however, the point sets are often sub- ject to sampling biases due to variable or incon- sistent observation actions. These biases can fun- damentally change the observed distributions of points and distort the results of learning. In this paper we propose the use of conditional diver- gences to correct these distortions and learn from biased point sets effectively. Our empirical study shows that the proposed method can successfully correct the biases and achieve satisfactory learn- ing performance.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-university14a, title = {Learning from Point Sets with Observational Bias}, author = {University, Liang Xiong Carnegie Mellon and University, Jeff Schneider Carnegie Mellon}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {37--45}, 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/university14a/university14a.pdf}, url = {https://proceedings.mlr.press/r12/university14a.html}, abstract = {Many objects can be represented as sets of multi- dimensional points. A common approach to learning from these point sets is to assume that each set is an i.i.d. sample from an unknown un- derlying distribution, and then estimate the sim- ilarities between these distributions. In realistic situations, however, the point sets are often sub- ject to sampling biases due to variable or incon- sistent observation actions. These biases can fun- damentally change the observed distributions of points and distort the results of learning. In this paper we propose the use of conditional diver- gences to correct these distortions and learn from biased point sets effectively. Our empirical study shows that the proposed method can successfully correct the biases and achieve satisfactory learn- ing performance.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Learning from Point Sets with Observational Bias %A Liang Xiong Carnegie Mellon University %A Jeff Schneider Carnegie Mellon University %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-university14a %I PMLR %P 37--45 %U https://proceedings.mlr.press/r12/university14a.html %V R12 %X Many objects can be represented as sets of multi- dimensional points. A common approach to learning from these point sets is to assume that each set is an i.i.d. sample from an unknown un- derlying distribution, and then estimate the sim- ilarities between these distributions. In realistic situations, however, the point sets are often sub- ject to sampling biases due to variable or incon- sistent observation actions. These biases can fun- damentally change the observed distributions of points and distort the results of learning. In this paper we propose the use of conditional diver- gences to correct these distortions and learn from biased point sets effectively. Our empirical study shows that the proposed method can successfully correct the biases and achieve satisfactory learn- ing performance. %Z Reissued by PMLR on 04 October 2026.
APA
University, L.X.C.M. & University, J.S.C.M.. (2014). Learning from Point Sets with Observational Bias. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:37-45 Available from https://proceedings.mlr.press/r12/university14a.html. Reissued by PMLR on 04 October 2026.

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