2024/01/31 by Xiaodong Wu, Wu, Xiaodong, Yufei Han +9
Computer Science · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #I.2.6 #Intelligent Tutoring Systems and Adaptive Learning #Machine Learning (cs.LG) #Machine Learning and Data Classification #Online Learning and Analytics
paper · pdf · doi:10.48550/arxiv.2401.17865
openalex publication_date 2024/01/31 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Machine teaching often involves the creation of an optimal (typically minimal) dataset to help a model (referred to as the `student') achieve specific goals given by a teacher. While abundant in the continuous domain, the studies on the effectiveness of machine teaching in the discrete domain are relatively limited. This paper focuses on machine teaching in the discrete domain, specifically on manipulating student models' predictions based on the goals of teachers via changing the training data efficiently. We formulate this task as a combinatorial optimization problem and solve it by proposing an iterative searching algorithm. Our algorithm demonstrates significant numerical merit in the scenarios where a teacher attempts at correcting erroneous predictions to improve the student's models, or maliciously manipulating the model to misclassify some specific samples to the target class aligned with his personal profits. Experimental results show that our proposed algorithm can have superior performance in effectively and efficiently manipulating the predictions of the model, surpassing conventional baselines.