Towards Reliable Empirical Machine Unlearning Evaluation: A Game-Theoretic View
- URL: http://arxiv.org/abs/2404.11577v2
- Date: Wed, 12 Jun 2024 08:04:58 GMT
- Title: Towards Reliable Empirical Machine Unlearning Evaluation: A Game-Theoretic View
- Authors: Yiwen Tu, Pingbang Hu, Jiaqi Ma,
- Abstract summary: 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.
- Score: 5.724350004671127
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
Related papers
- 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) - Adversarial Machine Unlearning [26.809123658470693]
This paper focuses on the challenge of machine unlearning, aiming to remove the influence of specific training data on machine learning models.
Traditionally, the development of unlearning algorithms runs parallel with that of membership inference attacks (MIA), a type of privacy threat.
We propose a game-theoretic framework that integrates MIAs into the design of unlearning algorithms.
arXiv Detail & Related papers (2024-06-11T20:07:22Z) - Gone but Not Forgotten: Improved Benchmarks for Machine Unlearning [0.0]
We describe and propose alternative evaluation methods for machine unlearning algorithms.
We show the utility of our alternative evaluations via a series of experiments of state-of-the-art unlearning algorithms on different computer vision datasets.
arXiv Detail & Related papers (2024-05-29T15:53:23Z) - Machine unlearning through fine-grained model parameters perturbation [26.653596302257057]
We propose fine-grained Top-K and Random-k parameters perturbed inexact machine unlearning strategies.
We also tackle the challenge of evaluating the effectiveness of machine unlearning.
arXiv Detail & Related papers (2024-01-09T07:14:45Z) - DUCK: Distance-based Unlearning via Centroid Kinematics [40.2428948628001]
This work introduces a novel unlearning algorithm, denoted as Distance-based Unlearning via Centroid Kinematics (DUCK)
evaluation of the algorithm's performance is conducted across various benchmark datasets.
We also introduce a novel metric, called Adaptive Unlearning Score (AUS), encompassing not only the efficacy of the unlearning process in forgetting target data but also quantifying the performance loss relative to the original model.
arXiv Detail & Related papers (2023-12-04T17:10:25Z) - 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) - Low-Regret Active learning [64.36270166907788]
We develop an online learning algorithm for identifying unlabeled data points that are most informative for training.
At the core of our work is an efficient algorithm for sleeping experts that is tailored to achieve low regret on predictable (easy) instances.
arXiv Detail & Related papers (2021-04-06T22:53:45Z) - DEALIO: Data-Efficient Adversarial Learning for Imitation from
Observation [57.358212277226315]
In imitation learning from observation IfO, a learning agent seeks to imitate a demonstrating agent using only observations of the demonstrated behavior without access to the control signals generated by the demonstrator.
Recent methods based on adversarial imitation learning have led to state-of-the-art performance on IfO problems, but they typically suffer from high sample complexity due to a reliance on data-inefficient, model-free reinforcement learning algorithms.
This issue makes them impractical to deploy in real-world settings, where gathering samples can incur high costs in terms of time, energy, and risk.
We propose a more data-efficient IfO algorithm
arXiv Detail & Related papers (2021-03-31T23:46:32Z) - Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory
to Learning Algorithms [91.3755431537592]
We analyze four broad meta-learning strategies which rely on plug-in estimation and pseudo-outcome regression.
We highlight how this theoretical reasoning can be used to guide principled algorithm design and translate our analyses into practice.
arXiv Detail & Related papers (2021-01-26T17:11:40Z) - Interpretable Off-Policy Evaluation in Reinforcement Learning by
Highlighting Influential Transitions [48.91284724066349]
Off-policy evaluation in reinforcement learning offers the chance of using observational data to improve future outcomes in domains such as healthcare and education.
Traditional measures such as confidence intervals may be insufficient due to noise, limited data and confounding.
We develop a method that could serve as a hybrid human-AI system, to enable human experts to analyze the validity of policy evaluation estimates.
arXiv Detail & Related papers (2020-02-10T00:26:43Z)
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.