Machine Unlearning
71 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Machine Unlearning
Most implemented papers
Certifiable Machine Unlearning for Linear Models
In this paper, we present an experimental study of the three state-of-the-art approximate unlearning methods for linear models and demonstrate the trade-offs between efficiency, effectiveness and certifiability offered by each method.
Machine Unlearning of Features and Labels
In this paper, we propose the first method for unlearning features and labels.
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning
The right to erasure requires removal of a user's information from data held by organizations, with rigorous interpretations extending to downstream products such as learned models.
Unrolling SGD: Understanding Factors Influencing Machine Unlearning
In this work, we first taxonomize approaches and metrics of approximate unlearning.
Fast Yet Effective Machine Unlearning
In the impair step, the noise matrix along with a very high learning rate is used to induce sharp unlearning in the model.
Zero-Shot Machine Unlearning
In case of machine learning (ML) applications, this necessitates deletion of data not only from storage archives but also from ML models.
Recommendation Unlearning
From the perspective of utility, if a system's utility is damaged by some bad data, the system needs to forget these data to regain utility.
Knowledge Removal in Sampling-based Bayesian Inference
In this paper, we propose the first machine unlearning algorithm for MCMC.
Deep Unlearning via Randomized Conditionally Independent Hessians
For models which require no training (k-NN), simply deleting the closest original sample can be effective.
Can Bad Teaching Induce Forgetting? Unlearning in Deep Networks using an Incompetent Teacher
It facilitates the provision for removal of certain set or class of data from an already trained ML model without requiring retraining from scratch.