To leverage prediction models to make optimal scheduling decisions in service
systems, we must understand how predictive errors impact congestion due to
externalities on the delay of other jobs. Motivated by applications where
prediction models interact with human servers (e.g., content moderation), we
consider a large queueing system comprising of many single server queues where
the class of a job is estimated using a prediction model. By characterizing the
impact of mispredictions on congestion cost in heavy traffic, we design an
index-based policy that incorporates the predicted class information in a
near-optimal manner. Our theoretical results guide the design of predictive
models by providing a simple model selection procedure with downstream queueing
performance as a central concern, and offer novel insights on how to design
queueing systems with AI-based triage. We illustrate our framework on a content
moderation task based on real online comments, where we construct toxicity
classifiers by finetuning large language models.

基于预测模型的最优调度决策在服务系统中的关键是理解预测误差对其他作业的延迟产生的外部性对拥塞的影响。我们在应用中考虑了预测模型与人类服务器交互的情况（例如内容审查），设计了一个基于指标的策略，以近乎最优的方式结合了预测的类别信息，通过对重负载下拥塞成本的特征化来指导预测模型的设计，并针对基于人工智能的任务划分设计了一种新颖的排队系统。我们以实际在线评论为基础，通过微调大型语言模型构建鉴别有毒性的分类器，展示了我们的框架。

基于人工智能的排队系统的设计和调度

Design and Scheduling of an AI-based Queueing System

Online advertising driven by auctions brings billions of dollars in revenue
for social networking services and e-commerce platforms. GSP auctions, which
are simple and easy to understand for advertisers, have almost become the
benchmark for ad auction mechanisms in the industry. However, most GSP-based
industrial practices assume that the user click only relies on the ad itself,
which overlook the effect of external items, referred to as externalities.
Recently, DNA has attempted to upgrade GSP with deep neural networks and models
local externalities to some extent. However, it only considers set-level
contexts from auctions and ignores the order and displayed position of ads,
which is still suboptimal. Although VCG-based multi-slot auctions (e.g., VCG,
WVCG) make it theoretically possible to model global externalities (e.g., the
order and positions of ads and so on), they lack an efficient balance of both
revenue and social welfare. In this paper, we propose novel auction mechanisms
named Neural Multi-slot Auctions (NMA) to tackle the above-mentioned
challenges. Specifically, we model the global externalities effectively with a
context-aware list-wise prediction module to achieve better performance. We
design a list-wise deep rank module to guarantee incentive compatibility in
end-to-end learning. Furthermore, we propose an auxiliary loss for social
welfare to effectively reduce the decline of social welfare while maximizing
revenue. Experiment results on both offline large-scale datasets and online A/B
tests demonstrate that NMA obtains higher revenue with balanced social welfare
than other existing auction mechanisms (i.e., GSP, DNA, WVCG) in industrial
practice, and we have successfully deployed NMA on Meituan food delivery
platform.

本文提出了一种新的拍卖机制 —— 神经多插槽拍卖（NMA），通过上下文感知列表式预测模块和深度排序模块有效地对广告的全球影响进行建模，同时设计一个辅助损失以有效地降低社会福利的下降，实现了最大化收入和平衡的社会福利，证实了 NMA 在收入和社会福利方面具有更好的性能。

NMA: 带外部性的神经多槽位拍卖在线广告

NMA: Neural Multi-slot Auctions with Externalities for Online Advertising

While the stable marriage problem and its variants model a vast range of
matching markets, they fail to capture complex agent relationships, such as the
affiliation of applicants and employers in an interview marketplace. To model
this problem, the existing literature on matching with externalities permits
agents to provide complete and total rankings over matchings based off of both
their own and their affiliates' matches. This complete ordering restriction is
unrealistic, and further the model may have an empty core. To address this, we
introduce the Dichotomous Affiliate Stable Matching (DASM) Problem, where
agents' preferences indicate dichotomous acceptance or rejection of another
agent in the marketplace, both for themselves and their affiliates.
We also assume the agent's preferences over entire matchings are determined
by a general weighted valuation function of their (and their affiliates')
matches. Our results are threefold: (1) we use a human study to show that
real-world matching rankings follow our assumed valuation function; (2) we
prove that there always exists a stable solution by providing an efficient,
easily-implementable algorithm that finds such a solution; and (3) we
experimentally validate the efficiency of our algorithm versus a
linear-programming-based approach.

本文提出 Dichotomous Affiliate Stable Matching (DASM) 问题，通过权衡代理人和其关联方的接受或拒绝并基于加权估值函数来解决匹配市场偏好问题，通过人类研究和算法验证，证明该方法的高效性。