Towards Reliable Empirical Machine Unlearning Evaluation: A Game-Theoretic View

Machine unlearning is the process of updating machine learning models to remove the information of specific training data samples, in order to comply with data protection regulations that allow individuals to request the removal of their personal data. Despite the recent development of numerous unlearning algorithms, reliable evaluation of these algorithms remains an open research question. In this work, we focus on membership inference attack (MIA) based evaluation, one of the most common approaches for evaluating unlearning algorithms, and address various pitfalls of existing evaluation metrics that lack reliability. Specifically, we propose a game-theoretic framework that formalizes the evaluation process as a game between unlearning algorithms and MIA adversaries, measuring the data removal efficacy of unlearning algorithms by the capability of the MIA adversaries. Through careful design of the game, we demonstrate that the natural evaluation metric induced from the game enjoys provable guarantees that the existing evaluation metrics fail to satisfy. Furthermore, we propose a practical and efficient algorithm to estimate the evaluation metric induced from the game, and demonstrate its effectiveness through both theoretical analysis and empirical experiments. This work presents a novel and reliable approach to empirically evaluating unlearning algorithms, paving the way for the development of more effective unlearning techniques.

Travel the Same Path: A Novel TSP Solving Strategy

In this paper, we provide a novel strategy for solving Traveling Salesman Problem, which is a famous combinatorial optimization problem studied intensely in the TCS community. In particular, we consider the imitation learning framework, which helps a deterministic algorithm making good choices whenever it needs to, resulting in a speed up while maintaining the exactness of the solution without suffering from the unpredictability and a potential large deviation. Furthermore, we demonstrate a strong generalization ability of a graph neural network trained under the imitation learning framework. Specifically, the model is capable of solving a large instance of TSP faster than the baseline while has only seen small TSP instances when training.