Postdoc, The University of Melbourne
2 papers at NeurIPS 2025
We propose AdaptDel, a randomized smoothing technique with adaptable deletion rates, achieving higher certified accuracy for sequence classification under edit distance perturbations, outperforming previous methods on diverse NLP tasks.
We obtain the first generalization guarnatees for adaptive data analysis in a setting where data grows over time.
