Label-Agnostic Forgetting: A Supervision-Free Unlearning in Deep Models

Machine unlearning aims to remove information derived from forgotten data while preserving that of the remaining dataset in a well-trained model. With the increasing emphasis on data privacy, several approaches to machine unlearning have emerged. However, these methods typically rely on complete supervision throughout the unlearning process. Unfortunately, obtaining such supervision, whether for the forgetting or remaining data, can be impractical due to the substantial cost associated with annotating real-world datasets. This challenge prompts us to propose a supervision-free unlearning approach that operates without the need for labels during the unlearning process. Specifically, we introduce a variational approach to approximate the distribution of representations for the remaining data. Leveraging this approximation, we adapt the original model to eliminate information from the forgotten data at the representation level. To further address the issue of lacking supervision information, which hinders alignment with ground truth, we introduce a contrastive loss to facilitate the matching of representations between the remaining data and those of the original model, thus preserving predictive performance. Experimental results across various unlearning tasks demonstrate the effectiveness of our proposed method, Label-Agnostic Forgetting (LAF) without using any labels, which achieves comparable performance to state-of-the-art methods that rely on full supervision information. Furthermore, our approach excels in semi-supervised scenarios, leveraging limited supervision information to outperform fully supervised baselines. This work not only showcases the viability of supervision-free unlearning in deep models but also opens up a new possibility for future research in unlearning at the representation level.

A Boosting Algorithm for Positive-Unlabeled Learning

Positive-unlabeled (PU) learning deals with binary classification problems when only positive (P) and unlabeled (U) data are available. A lot of PU methods based on linear models and neural networks have been proposed; however, there still lacks study on how the theoretically sound boosting-style algorithms could work with P and U data. Considering that in some scenarios when neural networks cannot perform as good as boosting algorithms even with fully-supervised data, we propose a novel boosting algorithm for PU learning: Ada-PU, which compares against neural networks. Ada-PU follows the general procedure of AdaBoost while two different distributions of P data are maintained and updated. After a weak classifier is learned on the newly updated distribution, the corresponding combining weight for the final ensemble is estimated using only PU data. We demonstrated that with a smaller set of base classifiers, the proposed method is guaranteed to keep the theoretical properties of boosting algorithm. In experiments, we showed that Ada-PU outperforms neural networks on benchmark PU datasets. We also study a real-world dataset UNSW-NB15 in cyber security and demonstrated that Ada-PU has superior performance for malicious activities detection.