6/6
In conclusion, RELOAD is an effective algorithm for unlearning arbitrary parts of the training set, and provides strong privacy guarantees for forgotten data.

5/6
Using TabNet attention masks we show how RELOAD removes dependence of model inference on forgotten features.

4/6
We conducted experiments on forgetting random samples and entire features from the training set, consistently outperforming unlearning baselines and protecting user privacy.

3/6
Key Idea: We compare cached end-of-training gradients to those on the remaining data to identify parameters in the model to reset.

2/6
Key Motivation: In unlearning, we typically require access to the set of data being forgotten. How can we unlearn, without that data?

1/6
Presenting "Unlearning Tabular Data without a 'Forget Set'"! We explore a new unlearning algorithm RELOAD in tabular learning. Drop by @neuripsconf.bsky.social Workshop on Table Representation Learning (@trl-research.bsky.social):
- SAT 14 Dec from 2:30pm-3:15pm!
- East Meeting Room 11-12