Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach

• URL: http://arxiv.org/abs/2501.19403v1
• Date: Fri, 31 Jan 2025 18:58:43 GMT
• Title: Redefining Machine Unlearning: A Conformal Prediction-Motivated Approach
• Authors: Yingdan Shi, Ren Wang,
• Abstract summary: We identify critical limitations in existing unlearning metrics and propose enhanced evaluation metrics inspired by conformal prediction.<n>Our metrics can effectively capture the extent to which ground truth labels are excluded from the prediction set.<n>We propose an unlearning framework that integrates conformal prediction insights into Carlini & Wagner adversarial attack loss.
• Score: 1.3731623617634434
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine unlearning seeks to systematically remove specified data from a trained model, effectively achieving a state as though the data had never been encountered during training. While metrics such as Unlearning Accuracy (UA) and Membership Inference Attack (MIA) provide a baseline for assessing unlearning performance, they fall short of evaluating the completeness and reliability of forgetting. This is because the ground truth labels remain potential candidates within the scope of uncertainty quantification, leaving gaps in the evaluation of true forgetting. In this paper, we identify critical limitations in existing unlearning metrics and propose enhanced evaluation metrics inspired by conformal prediction. Our metrics can effectively capture the extent to which ground truth labels are excluded from the prediction set. Furthermore, we observe that many existing machine unlearning methods do not achieve satisfactory forgetting performance when evaluated with our new metrics. To address this, we propose an unlearning framework that integrates conformal prediction insights into Carlini & Wagner adversarial attack loss. Extensive experiments on the image classification task demonstrate that our enhanced metrics offer deeper insights into unlearning effectiveness, and that our unlearning framework significantly improves the forgetting quality of unlearning methods.

Related papers

• Are We Truly Forgetting? A Critical Re-examination of Machine Unlearning Evaluation Protocols [14.961054239793356]
  We conduct a new comprehensive evaluation that employs representation-based evaluations of unlearned model under large-scale scenarios. Our analysis reveals that current state-of-the-art unlearning approaches either completely degrade the representational quality of the unlearned model. We introduce a novel unlearning evaluation setup from a transfer learning perspective, in which the forget set classes exhibit semantic similarity to downstream task classes.
  arXiv  Detail & Related papers  (2025-03-10T07:11:34Z)
• A Closer Look at Machine Unlearning for Large Language Models [46.245404272612795]
  Large language models (LLMs) may memorize sensitive or copyrighted content, raising privacy and legal concerns. We discuss several issues in machine unlearning for LLMs and provide our insights on possible approaches.
  arXiv  Detail & Related papers  (2024-10-10T16:56:05Z)
• Unlearning with Control: Assessing Real-world Utility for Large Language Model Unlearning [97.2995389188179]
  Recent research has begun to approach large language models (LLMs) unlearning via gradient ascent (GA) Despite their simplicity and efficiency, we suggest that GA-based methods face the propensity towards excessive unlearning. We propose several controlling methods that can regulate the extent of excessive unlearning.
  arXiv  Detail & Related papers  (2024-06-13T14:41:00Z)
• An Information Theoretic Evaluation Metric For Strong Unlearning [20.143627174765985]
  We introduce the Information Difference Index (IDI), a novel white-box metric inspired by information theory. IDI quantifies retained information in intermediate features by measuring mutual information between those features and the labels to be forgotten. Our experiments demonstrate that IDI effectively measures the degree of unlearning across various datasets and architectures.
  arXiv  Detail & Related papers  (2024-05-28T06:57:01Z)
• Towards Reliable Empirical Machine Unlearning Evaluation: A Game-Theoretic View [5.724350004671127]
  We propose a game-theoretic framework that formalizes the evaluation process as a game between unlearning algorithms and MIA adversaries. We show that the evaluation metric induced from the game enjoys provable guarantees that the existing evaluation metrics fail to satisfy. This work presents a novel and reliable approach to empirically evaluating unlearning algorithms, paving the way for the development of more effective unlearning techniques.
  arXiv  Detail & Related papers  (2024-04-17T17:20:27Z)
• Towards Lifecycle Unlearning Commitment Management: Measuring Sample-level Approximate Unlearning Completeness [30.596695293390415]
  We introduce the task of Lifecycle Unlearning Commitment Management (LUCM) for approximate unlearning. We propose an efficient metric designed to assess the sample-level unlearning completeness. We show that this metric is able to serve as a tool for monitoring unlearning anomalies throughout the unlearning lifecycle.
  arXiv  Detail & Related papers  (2024-03-19T15:37:27Z)
• Credible Teacher for Semi-Supervised Object Detection in Open Scene [106.25850299007674]
  In Open Scene Semi-Supervised Object Detection (O-SSOD), unlabeled data may contain unknown objects not observed in the labeled data. It is detrimental to the current methods that mainly rely on self-training, as more uncertainty leads to the lower localization and classification precision of pseudo labels. We propose Credible Teacher, an end-to-end framework to prevent uncertain pseudo labels from misleading the model.
  arXiv  Detail & Related papers  (2024-01-01T08:19:21Z)
• Uncertainty Estimation by Fisher Information-based Evidential Deep Learning [61.94125052118442]
  Uncertainty estimation is a key factor that makes deep learning reliable in practical applications. We propose a novel method, Fisher Information-based Evidential Deep Learning ($mathcalI$-EDL) In particular, we introduce Fisher Information Matrix (FIM) to measure the informativeness of evidence carried by each sample, according to which we can dynamically reweight the objective loss terms to make the network more focused on the representation learning of uncertain classes.
  arXiv  Detail & Related papers  (2023-03-03T16:12:59Z)
• Evaluating Machine Unlearning via Epistemic Uncertainty [78.27542864367821]
  This work presents an evaluation of Machine Unlearning algorithms based on uncertainty. This is the first definition of a general evaluation of our best knowledge.
  arXiv  Detail & Related papers  (2022-08-23T09:37:31Z)
• Learning Uncertainty For Safety-Oriented Semantic Segmentation In Autonomous Driving [77.39239190539871]
  We show how uncertainty estimation can be leveraged to enable safety critical image segmentation in autonomous driving. We introduce a new uncertainty measure based on disagreeing predictions as measured by a dissimilarity function. We show experimentally that our proposed approach is much less computationally intensive at inference time than competing methods.
  arXiv  Detail & Related papers  (2021-05-28T09:23:05Z)
• Unsupervised Domain Adaptation for Speech Recognition via Uncertainty Driven Self-Training [55.824641135682725]
  Domain adaptation experiments using WSJ as a source domain and TED-LIUM 3 as well as SWITCHBOARD show that up to 80% of the performance of a system trained on ground-truth data can be recovered.
  arXiv  Detail & Related papers  (2020-11-26T18:51:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.