Title: A Parametric Contextual Online Learning Theory of Brokerage Show Chinese title

Title: 一种经纪的参数化上下文在线学习理论

Abstract: We study the role of contextual information in the online learning problem of brokerage between traders. In this sequential problem, at each time step, two traders arrive with secret valuations about an asset they wish to trade. The learner (a broker) suggests a trading (or brokerage) price based on contextual data about the asset and the market conditions. Then, the traders reveal their willingness to buy or sell based on whether their valuations are higher or lower than the brokerage price. A trade occurs if one of the two traders decides to buy and the other to sell, i.e., if the broker's proposed price falls between the smallest and the largest of their two valuations. We design algorithms for this problem and prove optimal theoretical regret guarantees under various standard assumptions. Show Chinese abstract

Abstract: 我们研究上下文信息在交易者之间经纪在线学习问题中的作用。 在这个序列问题中，每个时间步，两个交易者带着他们希望交易的资产的秘密估值到来。 学习者（经纪人）根据关于资产和市场状况的上下文数据建议一个交易（或经纪）价格。 然后，交易者根据他们的估值是否高于或低于经纪价格来揭示他们购买或出售的意愿。 如果其中一个交易者决定购买而另一个决定出售，即如果经纪人提出的价格介于他们的两个估值的最小值和最大值之间，则会发生交易。 我们为此问题设计了算法，并在各种标准假设下证明了最优的理论遗憾保证。