Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Efficient Attribute Unlearning: Towards Selective Removal of Input
  Attributes from Feature Representations   Recently, the enactment of privacy regulations has promoted the rise of the
machine unlearning paradigm. Existing studies of machine unlearning mainly
focus on sample-wise unlearning, such that a learnt model will not expose
user's privacy at the sample level. Yet we argue that such ability of selective
removal should also be presented at the attribute level, especially for the
attributes irrelevant to the main task, e.g., whether a person recognized in a
face recognition system wears glasses or the age range of that person. Through
a comprehensive literature review, it is found that existing studies on
attribute-related problems like fairness and de-biasing learning cannot address
the above concerns properly. To bridge this gap, we propose a paradigm of
selectively removing input attributes from feature representations which we
name `attribute unlearning'. In this paradigm, certain attributes will be
accurately captured and detached from the learned feature representations at
the stage of training, according to their mutual information. The particular
attributes will be progressively eliminated along with the training procedure
towards convergence, while the rest of attributes related to the main task are
preserved for achieving competitive model performance. Considering the
computational complexity during the training process, we not only give a
theoretically approximate training method, but also propose an acceleration
scheme to speed up the training process. We validate our method by spanning
several datasets and models and demonstrate that our design can preserve model
fidelity and reach prevailing unlearning efficacy with high efficiency. The
proposed unlearning paradigm builds a foundation for future machine unlearning
system and will become an essential component of the latest privacy-related
legislation.

8
The categories of this paper are: hep-ex
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
5
The categories of this paper are: gr-qc
9
The categories of this paper are: nlin
30
The categories of this paper are: q-alg
20
The categories of this paper are: acc-phys
14
The categories of this paper are: math-ph
6
The categories of this paper are: astro-ph
15
The categories of this paper are: nucl-ex
4
The categories of this paper are: cond-mat
31
The categories of this paper are: ao-sci
37
The categories of this paper are: supr-con
32
The categories of this paper are: atom-ph