Learning from Biased Data: A Semi-Parametric Approach

Patrice Bertail, Stephan Clémençon, Yannick Guyonvarch, Nathan Noiry
Proceedings of the 38th International Conference on Machine Learning, PMLR 139:803-812, 2021.

Abstract

We consider risk minimization problems where the (source) distribution $P_S$ of the training observations $Z_1, \ldots, Z_n$ differs from the (target) distribution $P_T$ involved in the risk that one seeks to minimize. Under the natural assumption that $P_S$ dominates $P_T$, \textit{i.e.} $P_T< \! \!

Cite this Paper

BibTeX
@InProceedings{pmlr-v139-bertail21a, title = {Learning from Biased Data: A Semi-Parametric Approach}, author = {Bertail, Patrice and Cl{\'e}men{\c{c}}on, Stephan and Guyonvarch, Yannick and Noiry, Nathan}, booktitle = {Proceedings of the 38th International Conference on Machine Learning}, pages = {803--812}, year = {2021}, editor = {Meila, Marina and Zhang, Tong}, volume = {139}, series = {Proceedings of Machine Learning Research}, month = {18--24 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v139/bertail21a/bertail21a.pdf}, url = {https://proceedings.mlr.press/v139/bertail21a.html}, abstract = {We consider risk minimization problems where the (source) distribution $P_S$ of the training observations $Z_1, \ldots, Z_n$ differs from the (target) distribution $P_T$ involved in the risk that one seeks to minimize. Under the natural assumption that $P_S$ dominates $P_T$, \textit{i.e.} $P_T< \! \!
Endnote
%0 Conference Paper %T Learning from Biased Data: A Semi-Parametric Approach %A Patrice Bertail %A Stephan Clémençon %A Yannick Guyonvarch %A Nathan Noiry %B Proceedings of the 38th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2021 %E Marina Meila %E Tong Zhang %F pmlr-v139-bertail21a %I PMLR %P 803--812 %U https://proceedings.mlr.press/v139/bertail21a.html %V 139 %X We consider risk minimization problems where the (source) distribution $P_S$ of the training observations $Z_1, \ldots, Z_n$ differs from the (target) distribution $P_T$ involved in the risk that one seeks to minimize. Under the natural assumption that $P_S$ dominates $P_T$, \textit{i.e.} $P_T< \! \!
APA
Bertail, P., Clémençon, S., Guyonvarch, Y. & Noiry, N.. (2021). Learning from Biased Data: A Semi-Parametric Approach. Proceedings of the 38th International Conference on Machine Learning, in Proceedings of Machine Learning Research 139:803-812 Available from https://proceedings.mlr.press/v139/bertail21a.html.

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