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

This manuscript portrays optimization as a process. In many practical
applications the environment is so complex that it is infeasible to lay out a
comprehensive theoretical model and use classical algorithmic theory and
mathematical optimization. It is necessary as well as beneficial to take a
robust approach, by applying an optimization method that learns as one goes
along, learning from experience as more aspects of the problem are observed.
This view of optimization as a process has become prominent in varied fields
and has led to some spectacular success in modeling and systems that are now
part of our daily lives.

本文研究将优化视为一种过程，强调学习方法的应用，提倡使用鲁棒性的算法并学习实践经验以应对实际应用中复杂环境下的问题。

在线凸优化简介

Introduction to Online Convex Optimization

We consider a robust approach to address uncertainty in model parameters in
Markov Decision Processes (MDPs), which are widely used to model dynamic
optimization in many applications. Most prior works consider the case where the
uncertainty on transitions related to different states is uncoupled and the
adversary is allowed to select the worst possible realization for each state
unrelated to others, potentially leading to highly conservative solutions. On
the other hand, the case of general uncertainty sets is known to be
intractable. We consider a factor model for probability transitions where the
transition probability is a linear function of a factor matrix that is
uncertain and belongs to a factor matrix uncertainty set. This is a fairly
general model of uncertainty in probability transitions, allowing the decision
maker to model dependence between probability transitions across different
states and it is significantly less conservative than prior approaches. We show
that under an underlying rectangularity assumption, we can efficiently compute
an optimal robust policy under the factor matrix uncertainty model.
Furthermore, we show that there is an optimal robust policy that is
deterministic, which is of interest from an interpretability standpoint. We
also introduce the robust counterpart of important structural results of
classical MDPs, including the maximum principle and Blackwell optimality, and
we provide a computational study to demonstrate the effectiveness of our
approach in mitigating the conservativeness of robust policies.

本论文利用因子模型处理 Markov 决策过程中的参数不确定性问题，提出了一种鲁棒性方法来有效计算最优策略，并在相关领域进行了实验研究。

鲁棒马尔可夫决策过程：超越矩形假设

Robust Markov Decision Process: Beyond Rectangularity

We present a robust approach for detecting intrinsic sentence importance in
news, by training on two corpora of document-summary pairs. When used for
single-document summarization, our approach, combined with the "beginning of
document" heuristic, outperforms a state-of-the-art summarizer and the
beginning-of-article baseline in both automatic and manual evaluations. These
results represent an important advance because in the absence of cross-document
repetition, single document summarizers for news have not been able to
consistently outperform the strong beginning-of-article baseline.

本研究提出了一种鲁棒的方法，通过训练文档 - 摘要配对的语料库来检测新闻中内在句子的重要性，并结合‘文档开头’启发式方法，用于单篇文档摘要，得到了比现有先进摘要算法和基线方法更好的结果，这是一个重要的进步。