RoLNiP: Robust Learning Using Noisy Pairwise Comparisons
This work addresses the challenge of noisy data in pairwise comparison tasks, which is incremental as it builds on existing robust learning methods.
The paper tackles the problem of learning robust classifiers from noisy pairwise comparisons by proposing sufficient conditions on the loss function for risk minimization to be robust to noise, and it experimentally shows that the approach outperforms state-of-the-art methods.
This paper presents a robust approach for learning from noisy pairwise comparisons. We propose sufficient conditions on the loss function under which the risk minimization framework becomes robust to noise in the pairwise similar dissimilar data. Our approach does not require the knowledge of noise rate in the uniform noise case. In the case of conditional noise, the proposed method depends on the noise rates. For such cases, we offer a provably correct approach for estimating the noise rates. Thus, we propose an end-to-end approach to learning robust classifiers in this setting. We experimentally show that the proposed approach RoLNiP outperforms the robust state-of-the-art methods for learning with noisy pairwise comparisons.