Recommender systems have been shown to be vulnerable to poisoning attacks,
where malicious data is injected into the dataset to cause the recommender
system to provide biased recommendations. To defend against such attacks,
various robust learning methods have been proposed. However, most methods are
model-specific or attack-specific, making them lack generality, while other
methods, such as adversarial training, are oriented towards evasion attacks and
thus have a weak defense strength in poisoning attacks.
In this paper, we propose a general method, Real-time Vicinal Defense (RVD),
which leverages neighboring training data to fine-tune the model before making
a recommendation for each user. RVD works in the inference phase to ensure the
robustness of the specific sample in real-time, so there is no need to change
the model structure and training process, making it more practical. Extensive
experimental results demonstrate that RVD effectively mitigates targeted
poisoning attacks across various models without sacrificing accuracy. Moreover,
the defensive effect can be further amplified when our method is combined with
other strategies.

在这篇论文中，我们提出了一种通用方法 Real-time Vicinal Defense（RVD），通过利用邻近的训练数据来对模型进行微调，从而在为每个用户做推荐之前确保特定样本的鲁棒性，在实时中具有防御强度，在不改变模型结构和训练过程的情况下更加实用，并且通过与其他策略的结合可以进一步增强防御效果。

通过实时邻近防御实现健壮的推荐

Toward Robust Recommendation via Real-time Vicinal Defense

To improve efficiency and reduce failures in autonomous vehicles, research
has focused on developing robust and safe learning methods that take into
account disturbances in the environment. Existing literature in robust
reinforcement learning poses the learning problem as a two player game between
the autonomous system and disturbances. This paper examines two different
algorithms to solve the game, Robust Adversarial Reinforcement Learning and
Neural Fictitious Self Play, and compares performance on an autonomous driving
scenario. We extend the game formulation to a semi-competitive setting and
demonstrate that the resulting adversary better captures meaningful
disturbances that lead to better overall performance. The resulting robust
policy exhibits improved driving efficiency while effectively reducing
collision rates compared to baseline control policies produced by traditional
reinforcement learning methods.

该研究比较了 Robust Adversarial Reinforcement Learning 和 Neural Fictitious Self Play 算法在自动驾驶场景下的性能表现，将学习问题定义为自主系统与环境干扰之间的两人博弈，扩展为半竞争的情况，结果表明对手更好地捕捉了有意义的干扰，从而产生了更好的驾驶效率和减少了与传统强化学习方法相比的碰撞率。