Remember what you want to forget: Algorithms for machine unlearning
We study the problem of unlearning datapoints from a learnt model. The learner first
receives a dataset $ S $ drawn iid from an unknown distribution, and outputs a model …
receives a dataset $ S $ drawn iid from an unknown distribution, and outputs a model …
Machine unlearning for random forests
Responding to user data deletion requests, removing noisy examples, or deleting corrupted
training data are just a few reasons for wanting to delete instances from a machine learning …
training data are just a few reasons for wanting to delete instances from a machine learning …
Muse: Machine unlearning six-way evaluation for language models
Language models (LMs) are trained on vast amounts of text data, which may include private
and copyrighted content. Data owners may request the removal of their data from a trained …
and copyrighted content. Data owners may request the removal of their data from a trained …
Machine Unlearning: Taxonomy, Metrics, Applications, Challenges, and Prospects
Personal digital data is a critical asset, and governments worldwide have enforced laws and
regulations to protect data privacy. Data users have been endowed with the right to be …
regulations to protect data privacy. Data users have been endowed with the right to be …
Knowledge-adaptation priors
Humans and animals have a natural ability to quickly adapt to their surroundings, but
machine-learning models, when subjected to changes, often require a complete retraining …
machine-learning models, when subjected to changes, often require a complete retraining …
Incremental and decremental fuzzy bounded twin support vector machine
In this paper, we present an incremental variant of the Twin Support Vector Machine
(TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with …
(TWSVM) called Fuzzy Bounded Twin Support Vector Machine (FBTWSVM) to deal with …
[PDF][PDF] Exit through the training data: A look into instance-attribution explanations and efficient data deletion in machine learning
J Brophy - Technical report, 2020 - cs.uoregon.edu
The widespread use of machine learning models, coupled with large datasets and
increasingly complex models have led to a general lack of understanding for how individual …
increasingly complex models have led to a general lack of understanding for how individual …
Ticketed learning–unlearning schemes
We consider the learning–unlearning paradigm defined as follows. First given a dataset, the
goal is to learn a good predictor, such as one minimizing a certain loss. Subsequently, given …
goal is to learn a good predictor, such as one minimizing a certain loss. Subsequently, given …
[PDF][PDF] Parallelization of the incremental proximal support vector machine classifier using a heap-based tree topology
A Tveit, H Engum - Parallel and Distributed Computing for Machine …, 2003 - academia.edu
Support Vector Machines (SVMs) are an efficient data mining approach for classification,
clustering and time series analysis. In recent years, a tremendous growth in the amount of …
clustering and time series analysis. In recent years, a tremendous growth in the amount of …