Mechanism Design for Cost Optimal PAC Learning in the Presence of Strategic Noisy Annotators

Dinesh Garg, Sourangshu Bhattacharya, S. Sundararajan, Shirish Shevade
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:273-283, 2012.

Abstract

We consider the problem of Probably Approximate Correct (PAC) learning of a binary classifier from noisy labeled examples acquired from multiple annotators (each characterized by a respective classification noise rate). First, we consider the complete information scenario, where the learner knows the noise rates of all the annotators. For this scenario, we derive sample complexity bound for the Minimum Disagreement Algorithm (MDA) on the number of labeled examples to be obtained from each annotator. Next, we consider the incomplete information scenario, where each annotator is strategic and holds the respective noise rate as a private information. For this scenario, we design a cost optimal procurement auction mechanism along the lines of Myerson’s optimal auction design framework in a non-trivial manner. This mechanism satisfies incentive compatibility property, thereby facilitating the learner to elicit true noise rates of all the annotators.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-garg12a, title = {Mechanism Design for Cost Optimal {PAC} Learning in the Presence of Strategic Noisy Annotators}, author = {Garg, Dinesh and Bhattacharya, Sourangshu and Sundararajan, S. and Shevade, Shirish}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {273--283}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/garg12a/garg12a.pdf}, url = {https://proceedings.mlr.press/r10/garg12a.html}, abstract = {We consider the problem of Probably Approximate Correct (PAC) learning of a binary classifier from noisy labeled examples acquired from multiple annotators (each characterized by a respective classification noise rate). First, we consider the complete information scenario, where the learner knows the noise rates of all the annotators. For this scenario, we derive sample complexity bound for the Minimum Disagreement Algorithm (MDA) on the number of labeled examples to be obtained from each annotator. Next, we consider the incomplete information scenario, where each annotator is strategic and holds the respective noise rate as a private information. For this scenario, we design a cost optimal procurement auction mechanism along the lines of Myerson’s optimal auction design framework in a non-trivial manner. This mechanism satisfies incentive compatibility property, thereby facilitating the learner to elicit true noise rates of all the annotators.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Mechanism Design for Cost Optimal PAC Learning in the Presence of Strategic Noisy Annotators %A Dinesh Garg %A Sourangshu Bhattacharya %A S. Sundararajan %A Shirish Shevade %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-garg12a %I PMLR %P 273--283 %U https://proceedings.mlr.press/r10/garg12a.html %V R10 %X We consider the problem of Probably Approximate Correct (PAC) learning of a binary classifier from noisy labeled examples acquired from multiple annotators (each characterized by a respective classification noise rate). First, we consider the complete information scenario, where the learner knows the noise rates of all the annotators. For this scenario, we derive sample complexity bound for the Minimum Disagreement Algorithm (MDA) on the number of labeled examples to be obtained from each annotator. Next, we consider the incomplete information scenario, where each annotator is strategic and holds the respective noise rate as a private information. For this scenario, we design a cost optimal procurement auction mechanism along the lines of Myerson’s optimal auction design framework in a non-trivial manner. This mechanism satisfies incentive compatibility property, thereby facilitating the learner to elicit true noise rates of all the annotators. %Z Reissued by PMLR on 04 October 2026.
APA
Garg, D., Bhattacharya, S., Sundararajan, S. & Shevade, S.. (2012). Mechanism Design for Cost Optimal PAC Learning in the Presence of Strategic Noisy Annotators. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:273-283 Available from https://proceedings.mlr.press/r10/garg12a.html. Reissued by PMLR on 04 October 2026.

Related Material