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 underlying distribution, and then estimate the similarities between these distributions. In realistic situations, however, the point sets are often subject to sampling biases due to variable or inconsistent observation actions. These biases can fundamentally change the observed distributions of points and distort the results of learning. In this paper we propose the use of conditional divergences 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 learning 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 underlying distribution, and then estimate the similarities between these distributions. In realistic situations, however, the point sets are often subject to sampling biases due to variable or inconsistent observation actions. These biases can fundamentally change the observed distributions of points and distort the results of learning. In this paper we propose the use of conditional divergences 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 learning 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 underlying distribution, and then estimate the similarities between these distributions. In realistic situations, however, the point sets are often subject to sampling biases due to variable or inconsistent observation actions. These biases can fundamentally change the observed distributions of points and distort the results of learning. In this paper we propose the use of conditional divergences 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 learning 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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