动态交互学习的统一分析
A Unified Analysis of Dynamic Interactive Learning
摘要：
在本文中，我们研究的问题，学习演变的概念组合结构。Emamjomeh-Zadeh等人的先前工作[2020]将动态引入交互式学习，作为在聚类问题或推荐系统中对非静态用户偏好进行建模的一种方式。我们提供了许多有益的贡献，这个问题。首先，我们给出了一个框架，该框架捕获了[Emamjomeh-Zadeh等人，2020]，这使我们能够研究任何类型的概念演化，并匹配相同的查询复杂度界限和运行时间保证以前的模型。使用这个通用模型，我们解决了开放的问题，关闭查询复杂性的上限和下限之间的差距。最后，我们研究了一个有效的算法，学习者简单地按照反馈，在每一轮，我们提供错误的边界低直径图，如集团，明星，和一般的O（log n）直径图，通过使用马尔可夫链模型。
英文摘要：
In this paper we investigate the problem of learning evolving concepts over a combinatorial structure. Previous work by Emamjomeh-Zadeh et al. [2020] introduced dynamics into interactive learning as a way to model non-static user preferences in clustering problems or recommender systems. We provide many useful contributions to this problem. First, we give a framework that captures both of the models analyzed by [Emamjomeh-Zadeh et al., 2020], which allows us to study any type of concept evolution and matches the same query complexity bounds and running time guarantees of the previous models. Using this general model we solve the open problem of closing the gap between the upper and lower bounds on query complexity. Finally, we study an efficient algorithm where the learner simply follows the feedback at each round, and we provide mistake bounds for low diameter graphs such as cliques, stars, and general o(log n) diameter graphs by using a Markov Chain model.
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。
