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.

提出了一种学习噪声配对比较的强健方法，该方法根据损失函数提出了充分条件，使得风险最小化框架在类似数据中噪声强度变大情况下保持强健性，并通过提供估算噪声率的方法来解决条件噪声的情况，并最终实现学习强健分类器的端到端方法。

RoLNiP: 使用有噪声的配对比较进行鲁棒学习

RoLNiP: Robust Learning Using Noisy Pairwise Comparisons

In this paper, we study a classification problem in which sample labels are
randomly corrupted. In this scenario, there is an unobservable sample with
noise-free labels. However, before being observed, the true labels are
independently flipped with a probability $\rho\in[0,0.5)$, and the random label
noise can be class-conditional. Here, we address two fundamental problems
raised by this scenario. The first is how to best use the abundant surrogate
loss functions designed for the traditional classification problem when there
is label noise. We prove that any surrogate loss function can be used for
classification with noisy labels by using importance reweighting, with
consistency assurance that the label noise does not ultimately hinder the
search for the optimal classifier of the noise-free sample. The other is the
open problem of how to obtain the noise rate $\rho$. We show that the rate is
upper bounded by the conditional probability $P(y|x)$ of the noisy sample.
Consequently, the rate can be estimated, because the upper bound can be easily
reached in classification problems. Experimental results on synthetic and real
datasets confirm the efficiency of our methods.

本文研究了一个分类问题，其中样本标签被随机损坏。我们解决了如何在有标签噪声的情况下最好地利用传统分类问题的丰富代理损失函数，通过重要性重新加权来使用任何代理损失函数进行带有噪声标签的分类，以及如何获得噪声率的问题。