Generalization error bounds for learning to rank: Does the length of document lists matter?

Ambuj Tewari, Sougata Chaudhuri
Proceedings of the 32nd International Conference on Machine Learning, PMLR 37:315-323, 2015.

Abstract

We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily degrade as the size of the document list associated with a query increases. We show that such a degradation is not intrinsic to the problem. For several loss functions, including the cross-entropy loss used in the well known ListNet method, there is no degradation in generalization ability as document lists become longer. We also provide novel generalization error bounds under \ell_1 regularization and faster convergence rates if the loss function is smooth.

Cite this Paper

BibTeX
@InProceedings{pmlr-v37-tewari15, title = {Generalization error bounds for learning to rank: Does the length of document lists matter?}, author = {Tewari, Ambuj and Chaudhuri, Sougata}, booktitle = {Proceedings of the 32nd International Conference on Machine Learning}, pages = {315--323}, year = {2015}, editor = {Bach, Francis and Blei, David}, volume = {37}, series = {Proceedings of Machine Learning Research}, address = {Lille, France}, month = {07--09 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v37/tewari15.pdf}, url = {https://proceedings.mlr.press/v37/tewari15.html}, abstract = {We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily degrade as the size of the document list associated with a query increases. We show that such a degradation is not intrinsic to the problem. For several loss functions, including the cross-entropy loss used in the well known ListNet method, there is no degradation in generalization ability as document lists become longer. We also provide novel generalization error bounds under \ell_1 regularization and faster convergence rates if the loss function is smooth.} }
Endnote
%0 Conference Paper %T Generalization error bounds for learning to rank: Does the length of document lists matter? %A Ambuj Tewari %A Sougata Chaudhuri %B Proceedings of the 32nd International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2015 %E Francis Bach %E David Blei %F pmlr-v37-tewari15 %I PMLR %P 315--323 %U https://proceedings.mlr.press/v37/tewari15.html %V 37 %X We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily degrade as the size of the document list associated with a query increases. We show that such a degradation is not intrinsic to the problem. For several loss functions, including the cross-entropy loss used in the well known ListNet method, there is no degradation in generalization ability as document lists become longer. We also provide novel generalization error bounds under \ell_1 regularization and faster convergence rates if the loss function is smooth.
RIS
TY - CPAPER TI - Generalization error bounds for learning to rank: Does the length of document lists matter? AU - Ambuj Tewari AU - Sougata Chaudhuri BT - Proceedings of the 32nd International Conference on Machine Learning DA - 2015/06/01 ED - Francis Bach ED - David Blei ID - pmlr-v37-tewari15 PB - PMLR DP - Proceedings of Machine Learning Research VL - 37 SP - 315 EP - 323 L1 - http://proceedings.mlr.press/v37/tewari15.pdf UR - https://proceedings.mlr.press/v37/tewari15.html AB - We consider the generalization ability of algorithms for learning to rank at a query level, a problem also called subset ranking. Existing generalization error bounds necessarily degrade as the size of the document list associated with a query increases. We show that such a degradation is not intrinsic to the problem. For several loss functions, including the cross-entropy loss used in the well known ListNet method, there is no degradation in generalization ability as document lists become longer. We also provide novel generalization error bounds under \ell_1 regularization and faster convergence rates if the loss function is smooth. ER -
APA
Tewari, A. & Chaudhuri, S.. (2015). Generalization error bounds for learning to rank: Does the length of document lists matter?. Proceedings of the 32nd International Conference on Machine Learning, in Proceedings of Machine Learning Research 37:315-323 Available from https://proceedings.mlr.press/v37/tewari15.html.

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