准确性的市场：竞争下的分类
A Market for Accuracy: Classification under Competition
AI总结：
针对传统机器学习忽略服务提供商间竞争的问题，提出一种竞争下的分类方法，使学习者能最大化市场份额，并证明该方法惠及供需双方且能使市场快速收敛至均衡。
摘要：
机器学习模型对于希望在消费市场中获取市场份额的服务提供商起着关键作用。然而，传统的学习方法并未考虑到争夺消费者的其他提供商的存在。我们的工作旨在研究这种市场环境中的学习问题，因为它会影响提供商、消费者以及市场本身。我们首先通过学习目标的视角分析此类市场，并表明准确性不能是唯一的考量因素。随后，我们提出了一种竞争下的分类方法，使得学习者能够在存在竞争者的情况下最大化市场份额。我们表明，我们的方法使提供商和消费者均受益，并发现市场进入时机和模型更新可能至关重要。我们展示了该方法在从简单分布到噪声数据集等一系列领域的有效性，并表明整个市场通过快速收敛至均衡而保持稳定。
英文摘要：
Machine learning models play a key role for service providers looking to gain market share in consumer markets. However, traditional learning approaches do not take into account the existence of additional providers, who compete with each other for consumers. Our work aims to study learning in this market setting, as it affects providers, consumers, and the market itself. We begin by analyzing such markets through the lens of the learning objective, and show that accuracy cannot be the only consideration. We then propose a method for classification under competition, so that a learner can maximize market share in the presence of competitors. We show that our approach benefits the providers as well as the consumers, and find that the timing of market entry and model updates can be crucial. We display the effectiveness of our approach across a range of domains, from simple distributions to noisy datasets, and show that the market as a whole remains stable by converging quickly to an equilibrium.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
