Data-driven algorithm design is a paradigm that uses statistical and machine
learning techniques to select from a class of algorithms for a computational
problem an algorithm that has the best expected performance with respect to
some (unknown) distribution on the instances of the problem. We build upon
recent work in this line of research by introducing the idea where, instead of
selecting a single algorithm that has the best performance, we allow the
possibility of selecting an algorithm based on the instance to be solved. In
particular, given a representative sample of instances, we learn a neural
network that maps an instance of the problem to the most appropriate algorithm
{\em for that instance}. We formalize this idea and derive rigorous sample
complexity bounds for this learning problem, in the spirit of recent work in
data-driven algorithm design. We then apply this approach to the problem of
making good decisions in the branch-and-cut framework for mixed-integer
optimization (e.g., which cut to add?). In other words, the neural network will
take as input a mixed-integer optimization instance and output a decision that
will result in a small branch-and-cut tree for that instance. Our computational
results provide evidence that our particular way of using neural networks for
cut selection can make a significant impact in reducing branch-and-cut tree
sizes, compared to previous data-driven approaches.

数据驱动算法设计通过使用统计和机器学习技术，从一个算法类中选择一个在某个（未知）分布中期望表现最佳的算法。该论文提出了一种在解决问题时可以基于问题实例选择算法的思想，并应用于混合整数优化的分支切割框架中，通过神经网络在决策中起到重要作用。

基于数据驱动的神经网络算法设计及其在分支定界中的应用

Data-driven algorithm design using neural networks with applications to  branch-and-cut

In the domain of Vehicular Ad hoc Networks (VANETs), where the imperative of
having a real-world malicious detector capable of detecting attacks in
real-time and unveiling their perpetrators is crucial, our study introduces a
system with this goal. This system is designed for real-time detection of
malicious behavior, addressing the critical need to first identify the onset of
attacks and subsequently the responsible actors. Prior work in this area have
never addressed both requirements, which we believe are necessary for real
world deployment, simultaneously. By seamlessly integrating statistical and
machine learning techniques, the proposed system prioritizes simplicity and
efficiency. It excels in swiftly detecting attack onsets with a remarkable
F1-score of 99.66%, subsequently identifying malicious vehicles with an average
F1-score of approximately 97.85%. Incorporating federated learning in both
stages enhances privacy and improves the efficiency of malicious node
detection, effectively reducing the false negative rate.

提出了一种针对车辆自组织网络中恶意行为的实时检测系统，通过集成统计学和机器学习技术，以简单高效的方式快速检测攻击并识别恶意车辆，同时运用联邦学习提高隐私性和恶意节点检测的效率，以降低误报率。