Existing Literature about Machine Unlearning

Existing Literature about Machine Unlearning - GitHub

Existing Literature about Machine Unlearning. Contribute to jjbrophy47/machine_unlearning development by creating an account on GitHub.

Machine Unlearning: A Comprehensive Survey - arXiv

Specifically, machine unlearning is to make a trained model to remove the contribution of an erased subset of the training dataset. This survey ...

An overview of machine unlearning - ScienceDirect

... literature has focused on how to ... Finally, we discuss the future development of machine unlearning based on the existing research.

A Closer Look at Machine Unlearning for Large Language Models

Specifically, the behavior that untargeted unlearning attempts to approximate is unpredictable and may involve hallucinations, and existing ...

Machine Unlearning: A Survey - ACM Digital Library

They also provided a taxonomy of the existing unlearning schemes based on available models and data. •. Saurabh et al. [36] analyzed the problem of privacy ...

Learn to Unlearn: Insights Into Machine Unlearning

In the developing field of machine unlearning, there are discernible gaps and ambiguities in the existing literature. These gaps not only leave pressing ...

A fresh perspective on machine unlearning, with a real-world solution!

Machine unlearning refers to the process of mitigating the impact of specific training data points on a previously trained machine learning model.

Machine Unlearning in 2024 - Ken Ziyu Liu - Stanford AI Lab

The unlearning literature can roughly be categorized into the following: Exact unlearning; “Unlearning” via differential privacy; Empirical ...

Machine Unlearning | IEEE Conference Publication

Once users have shared their data online, it is generally difficult for them to revoke access and ask for the data to be deleted. Machine learning (ML) ...

3 Recommendations for Machine Unlearning Evaluation Challenges

While there is optimism about machine unlearning being a promising solution to many of the privacy and security challenges posed by AI, current ...

Algorithms that forget: Machine unlearning and the right to erasure

... existing literature. There are several works on machine unlearning, mostly from a technical perspective. There are also several pieces of literature, where ...

Fast Machine Unlearning without Retraining through Selective ...

This adds computational overhead and mandates that the training data remain available and accessible, which may not be feasible. In contrast, ...

Fair Machine Unlearning: Data Removal while Mitigating Disparities

In addition to brute-force retraining with. BCE loss (named as Retraining (BCE)), we consider three unlearning methods from existing literature. The three ...

Towards Unbounded Machine Unlearning - OpenReview

Using cross-entropy only for the forget-set yields the simple NegGrad in the existing literature, that tends to catastrophically forget everything, and ...

Announcing the first Machine Unlearning Challenge

Because of this, existing unlearning algorithms make different trade-offs. ... unlearning methods in the literature. This leaves us with a ...

Towards Making Systems Forget with Machine Unlearning

Forgetting systems are complementary to much existing work [55,75,80]. Systems such as Google Search [6] can forget a user's raw data upon request, but they ...

Ensuring User Privacy and Model Security via Machine Unlearning

In this paper, we present the first comprehensive survey of this realm. We summarize and categorize existing machine unlearning methods ...

Differential Privacy and Machine Unlearning'' by Aaron Roth (12/03)

Introduction: Today, we have Aaron Roth. He is a professor at Penn. Before Penn, he spent a year as a postdoc at Microsoft Research New ...

[PDF] Machine Unlearning: Solutions and Challenges

This paper categorizes existing solutions into exact unlearning approaches that remove data influence thoroughly and approximate unlearning approaches that ...

(PDF) Machine Unlearning: Solutions and Challenges - ResearchGate

This paper provides a comprehensive taxonomy and analysis of the solutions in machine unlearning. We categorize existing solutions into exact unlearning ...