Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions

Arpit Agarwal, Shivani Agarwal, Sanjeev Khanna, Prathamesh Patil
Proceedings of the 37th International Conference on Machine Learning, PMLR 119:85-95, 2020.

Abstract

Rank aggregation from pairwise preferences has widespread applications in recommendation systems and information retrieval. Given the enormous economic and societal impact of these applications, and the consequent incentives for malicious players to manipulate ranking outcomes in their favor, an important challenge is to make rank aggregation algorithms robust to adversarial manipulations in data. In this paper, we initiate the study of robustness in rank aggregation under the popular Bradley-Terry-Luce (BTL) model for pairwise comparisons. We consider a setting where pairwise comparisons are initially generated according to a BTL model, but a fraction of these comparisons are corrupted by an adversary prior to being reported to us. We consider a strong contamination model, where an adversary having complete knowledge of the initial truthful data and the underlying true BTL parameters, can subsequently corrupt the truthful data by inserting, deleting, or changing data points. The goal is to estimate the true score/weight of each item under the BTL model, even in the presence of these corruptions. We characterize the extent of adversarial corruption under which the true BTL parameters are uniquely identifiable. We also provide a novel pruning algorithm that provably cleans the data of adversarial corruption under reasonable conditions on data generation and corruption. We corroborate our theory with experiments on both synthetic as well as real data showing that previous algorithms are vulnerable to even small amounts of corruption, whereas our algorithm can clean a reasonably high amount of corruption.

Cite this Paper


BibTeX
@InProceedings{pmlr-v119-agarwal20a, title = {Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions}, author = {Agarwal, Arpit and Agarwal, Shivani and Khanna, Sanjeev and Patil, Prathamesh}, booktitle = {Proceedings of the 37th International Conference on Machine Learning}, pages = {85--95}, year = {2020}, editor = {III, Hal Daumé and Singh, Aarti}, volume = {119}, series = {Proceedings of Machine Learning Research}, month = {13--18 Jul}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v119/agarwal20a/agarwal20a.pdf}, url = {http://proceedings.mlr.press/v119/agarwal20a.html}, abstract = {Rank aggregation from pairwise preferences has widespread applications in recommendation systems and information retrieval. Given the enormous economic and societal impact of these applications, and the consequent incentives for malicious players to manipulate ranking outcomes in their favor, an important challenge is to make rank aggregation algorithms robust to adversarial manipulations in data. In this paper, we initiate the study of robustness in rank aggregation under the popular Bradley-Terry-Luce (BTL) model for pairwise comparisons. We consider a setting where pairwise comparisons are initially generated according to a BTL model, but a fraction of these comparisons are corrupted by an adversary prior to being reported to us. We consider a strong contamination model, where an adversary having complete knowledge of the initial truthful data and the underlying true BTL parameters, can subsequently corrupt the truthful data by inserting, deleting, or changing data points. The goal is to estimate the true score/weight of each item under the BTL model, even in the presence of these corruptions. We characterize the extent of adversarial corruption under which the true BTL parameters are uniquely identifiable. We also provide a novel pruning algorithm that provably cleans the data of adversarial corruption under reasonable conditions on data generation and corruption. We corroborate our theory with experiments on both synthetic as well as real data showing that previous algorithms are vulnerable to even small amounts of corruption, whereas our algorithm can clean a reasonably high amount of corruption.} }
Endnote
%0 Conference Paper %T Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions %A Arpit Agarwal %A Shivani Agarwal %A Sanjeev Khanna %A Prathamesh Patil %B Proceedings of the 37th International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2020 %E Hal Daumé III %E Aarti Singh %F pmlr-v119-agarwal20a %I PMLR %P 85--95 %U http://proceedings.mlr.press/v119/agarwal20a.html %V 119 %X Rank aggregation from pairwise preferences has widespread applications in recommendation systems and information retrieval. Given the enormous economic and societal impact of these applications, and the consequent incentives for malicious players to manipulate ranking outcomes in their favor, an important challenge is to make rank aggregation algorithms robust to adversarial manipulations in data. In this paper, we initiate the study of robustness in rank aggregation under the popular Bradley-Terry-Luce (BTL) model for pairwise comparisons. We consider a setting where pairwise comparisons are initially generated according to a BTL model, but a fraction of these comparisons are corrupted by an adversary prior to being reported to us. We consider a strong contamination model, where an adversary having complete knowledge of the initial truthful data and the underlying true BTL parameters, can subsequently corrupt the truthful data by inserting, deleting, or changing data points. The goal is to estimate the true score/weight of each item under the BTL model, even in the presence of these corruptions. We characterize the extent of adversarial corruption under which the true BTL parameters are uniquely identifiable. We also provide a novel pruning algorithm that provably cleans the data of adversarial corruption under reasonable conditions on data generation and corruption. We corroborate our theory with experiments on both synthetic as well as real data showing that previous algorithms are vulnerable to even small amounts of corruption, whereas our algorithm can clean a reasonably high amount of corruption.
APA
Agarwal, A., Agarwal, S., Khanna, S. & Patil, P.. (2020). Rank Aggregation from Pairwise Comparisons in the Presence of Adversarial Corruptions. Proceedings of the 37th International Conference on Machine Learning, in Proceedings of Machine Learning Research 119:85-95 Available from http://proceedings.mlr.press/v119/agarwal20a.html.

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