Unrolling SGD: Understanding Factors Influencing Machine Unlearning
- URL: http://arxiv.org/abs/2109.13398v1
- Date: Mon, 27 Sep 2021 23:46:59 GMT
- Title: Unrolling SGD: Understanding Factors Influencing Machine Unlearning
- Authors: Anvith Thudi, Gabriel Deza, Varun Chandrasekaran, Nicolas Papernot
- Abstract summary: Machine unlearning is the process through which a deployed machine learning model forgets about one of its training data points.
We first taxonomize approaches and metrics of approximate unlearning.
We identify verification error, i.e., the L2 difference between the weights of an approximately unlearned and a naively retrained model.
- Score: 17.6607904333012
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Machine unlearning is the process through which a deployed machine learning
model forgets about one of its training data points. While naively retraining
the model from scratch is an option, it is almost always associated with a
large computational effort for deep learning models. Thus, several approaches
to approximately unlearn have been proposed along with corresponding metrics
that formalize what it means for a model to forget about a data point. In this
work, we first taxonomize approaches and metrics of approximate unlearning. As
a result, we identify verification error, i.e., the L2 difference between the
weights of an approximately unlearned and a naively retrained model, as a
metric approximate unlearning should optimize for as it implies a large class
of other metrics. We theoretically analyze the canonical stochastic gradient
descent (SGD) training algorithm to surface the variables which are relevant to
reducing the verification error of approximate unlearning for SGD. From this
analysis, we first derive an easy-to-compute proxy for verification error
(termed unlearning error). The analysis also informs the design of a new
training objective penalty that limits the overall change in weights during SGD
and as a result facilitates approximate unlearning with lower verification
error. We validate our theoretical work through an empirical evaluation on
CIFAR-10, CIFAR-100, and IMDB sentiment analysis.
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