23 citations · 37 across the 13 of their papers we have counts for
6 papers · 1 filter
What Was That Again? Certified Robustness for Automatic Speech Recognition
Andrew C. Cullen, Neil G. Marchant, Jiani Xie +2
Automatic Speech Recognition systems are notoriously both sensitive to adversarial and benign perturbations. While this has been repeatedly demonstrated using reference datasets, d…
On the Bayes Inconsistency of Disagreement Discrepancy Surrogates
Neil G. Marchant, Andrew C. Cullen, Feng Liu +1
Deep neural networks often fail when deployed in real-world contexts due to distribution shift, a critical barrier to building safe and reliable systems. An emerging approach to ad…
Adaptive Data Analysis for Growing Data
Neil G. Marchant, Benjamin I. P. Rubinstein
Reuse of data in adaptive workflows poses challenges regarding overfitting and the statistical validity of results. Previous work has demonstrated that interacting with data via di…
Hard to Forget: Poisoning Attacks on Certified Machine Unlearning
Neil G. Marchant, Benjamin I. P. Rubinstein, Scott Alfeld
The right to erasure requires removal of a user's information from data held by organizations, with rigorous interpretations extending to downstream products such as learned models…
Needle in a Haystack: Label-Efficient Evaluation under Extreme Class Imbalance
Neil G. Marchant, Benjamin I. P. Rubinstein
Important tasks like record linkage and extreme classification demonstrate extreme class imbalance, with 1 minority instance to every 1 million or more majority instances. Obtainin…
In Search of an Entity Resolution OASIS: Optimal Asymptotic Sequential Importance Sampling
Neil G. Marchant, Benjamin I. P. Rubinstein
Entity resolution (ER) presents unique challenges for evaluation methodology. While crowdsourcing platforms acquire ground truth, sound approaches to sampling must drive labelling…