Provable (epsilon, delta)-unlearning algorithms for topic models achieve deletion capacity O~(m/(r^2 sqrt(nr))) before fine-tuning and O~(m q/(r sqrt(nr))) after fine-tuning, with the base model untouched in the downstream case.
Learning topic models - going beyond svd, 2012 b
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2024 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
Provable unlearning in topic modeling and downstream tasks
Provable (epsilon, delta)-unlearning algorithms for topic models achieve deletion capacity O~(m/(r^2 sqrt(nr))) before fine-tuning and O~(m q/(r sqrt(nr))) after fine-tuning, with the base model untouched in the downstream case.