$\nabla τ$: Gradient-based and Task-Agnostic machine Unlearning

• URL: http://arxiv.org/abs/2403.14339v1
• Date: Thu, 21 Mar 2024 12:11:26 GMT
• Title: $\nabla τ$: Gradient-based and Task-Agnostic machine Unlearning
• Authors: Daniel Trippa, Cesare Campagnano, Maria Sofia Bucarelli, Gabriele Tolomei, Fabrizio Silvestri,
• Abstract summary: We introduce Gradient-based and Task-Agnostic machine Unlearning ($nabla tau$) $nabla tau$ applies adaptive gradient ascent to the data to be forgotten while using standard gradient descent for the remaining data. We evaluate our framework's effectiveness using a set of well-established Membership Inference Attack metrics.
• Score: 7.04736023670375
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Machine Unlearning, the process of selectively eliminating the influence of certain data examples used during a model's training, has gained significant attention as a means for practitioners to comply with recent data protection regulations. However, existing unlearning methods face critical drawbacks, including their prohibitively high cost, often associated with a large number of hyperparameters, and the limitation of forgetting only relatively small data portions. This often makes retraining the model from scratch a quicker and more effective solution. In this study, we introduce Gradient-based and Task-Agnostic machine Unlearning ($\nabla \tau$), an optimization framework designed to remove the influence of a subset of training data efficiently. It applies adaptive gradient ascent to the data to be forgotten while using standard gradient descent for the remaining data. $\nabla \tau$ offers multiple benefits over existing approaches. It enables the unlearning of large sections of the training dataset (up to 30%). It is versatile, supporting various unlearning tasks (such as subset forgetting or class removal) and applicable across different domains (images, text, etc.). Importantly, $\nabla \tau$ requires no hyperparameter adjustments, making it a more appealing option than retraining the model from scratch. We evaluate our framework's effectiveness using a set of well-established Membership Inference Attack metrics, demonstrating up to 10% enhancements in performance compared to state-of-the-art methods without compromising the original model's accuracy.

Related papers

• Machine Unlearning on Pre-trained Models by Residual Feature Alignment Using LoRA [15.542668474378633]
  We propose a novel and efficient machine unlearning method on pre-trained models. We leverage LoRA to decompose the model's intermediate features into pre-trained features and residual features. The method aims to learn the zero residuals on the retained set and shifted residuals on the unlearning set.
  arXiv  Detail & Related papers  (2024-11-13T08:56:35Z)
• Towards Scalable Exact Machine Unlearning Using Parameter-Efficient Fine-Tuning [35.681853074122735]
  We introduce an exact unlearning framework -- Sequence-aware Sharded Sliced Training (S3T) S3T is designed to enhance the deletion capabilities of an exact unlearning system while minimizing the impact on model's performance. We demonstrate S3T attains superior deletion capabilities and enhanced performance compared to baselines across a wide range of settings.
  arXiv  Detail & Related papers  (2024-06-24T01:45:13Z)
• Partially Blinded Unlearning: Class Unlearning for Deep Networks a Bayesian Perspective [4.31734012105466]
  Machine Unlearning is the process of selectively discarding information designated to specific sets or classes of data from a pre-trained model. We propose a methodology tailored for the purposeful elimination of information linked to a specific class of data from a pre-trained classification network. Our novel approach, termed textbfPartially-Blinded Unlearning (PBU), surpasses existing state-of-the-art class unlearning methods, demonstrating superior effectiveness.
  arXiv  Detail & Related papers  (2024-03-24T17:33:22Z)
• Learn to Unlearn for Deep Neural Networks: Minimizing Unlearning Interference with Gradient Projection [56.292071534857946]
  Recent data-privacy laws have sparked interest in machine unlearning. Challenge is to discard information about the forget'' data without altering knowledge about remaining dataset. We adopt a projected-gradient based learning method, named as Projected-Gradient Unlearning (PGU) We provide empirically evidence to demonstrate that our unlearning method can produce models that behave similar to models retrained from scratch across various metrics even when the training dataset is no longer accessible.
  arXiv  Detail & Related papers  (2023-12-07T07:17:24Z)
• Recommendation Unlearning via Influence Function [42.4931807753579]
  We propose a new Influence Function-based Recommendation Unlearning (IFRU) framework, which efficiently updates the model without retraining. IFRU achieves more than 250 times acceleration compared to retraining-based methods with recommendation performance comparable to full retraining.
  arXiv  Detail & Related papers  (2023-07-05T09:42:51Z)
• Value function estimation using conditional diffusion models for control [62.27184818047923]
  We propose a simple algorithm called Diffused Value Function (DVF) It learns a joint multi-step model of the environment-robot interaction dynamics using a diffusion model. We show how DVF can be used to efficiently capture the state visitation measure for multiple controllers.
  arXiv  Detail & Related papers  (2023-06-09T18:40:55Z)
• CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning [55.733193075728096]
  Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate this data bias issue. We propose a meta-model capable of adaptively learning an explicit weighting scheme directly from data.
  arXiv  Detail & Related papers  (2022-02-11T13:49:51Z)
• Machine Unlearning of Features and Labels [72.81914952849334]
  We propose first scenarios for unlearning and labels in machine learning models. Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters.
  arXiv  Detail & Related papers  (2021-08-26T04:42:24Z)
• SSSE: Efficiently Erasing Samples from Trained Machine Learning Models [103.43466657962242]
  We propose an efficient and effective algorithm, SSSE, for samples erasure. In certain cases SSSE can erase samples almost as well as the optimal, yet impractical, gold standard of training a new model from scratch with only the permitted data.
  arXiv  Detail & Related papers  (2021-07-08T14:17:24Z)
• Model-agnostic and Scalable Counterfactual Explanations via Reinforcement Learning [0.5729426778193398]
  We propose a deep reinforcement learning approach that transforms the optimization procedure into an end-to-end learnable process. Our experiments on real-world data show that our method is model-agnostic, relying only on feedback from model predictions.
  arXiv  Detail & Related papers  (2021-06-04T16:54:36Z)
• AutoSimulate: (Quickly) Learning Synthetic Data Generation [70.82315853981838]
  We propose an efficient alternative for optimal synthetic data generation based on a novel differentiable approximation of the objective. We demonstrate that the proposed method finds the optimal data distribution faster (up to $50times$), with significantly reduced training data generation (up to $30times$) and better accuracy ($+8.7%$) on real-world test datasets than previous methods.
  arXiv  Detail & Related papers  (2020-08-16T11:36:11Z)

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.