23 citations · 37 across the 13 of their papers we have counts for
15 papers
Room for Error: Large-Scale Simulation of Over-the-Air Acoustic Attacks
Andrew C. Cullen, Neil G. Marchant, Jiani Xie +4
While voice control is rapidly becoming a ubiquitous vector of human-AI communication, the risks facing these systems remain poorly understood. This is, in part, a product of the d…
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…
Watermarks Attack Watermarks: Re-Watermarking as a Generic Removal Strategy
Maria Bulychev, Neil G. Marchant, Benjamin I. P. Rubinstein
Watermarking combines an imperceptible change to an input image that will trigger a detector, to assert provenance and protect intellectual property. The literature has shown great…
Where is the Watermark? Interpretable Watermark Detection at the Block Level
Maria Bulychev, Neil G. Marchant, Benjamin I. P. Rubinstein
Recent advances in generative AI have enabled the creation of highly realistic digital content, raising concerns around authenticity, ownership, and misuse. While watermarking has…
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…
AdaptDel: Adaptable Deletion Rate Randomized Smoothing for Certified Robustness
Zhuoqun Huang, Neil G. Marchant, Olga Ohrimenko +1
We consider the problem of certified robustness for sequence classification against edit distance perturbations. Naturally occurring inputs of varying lengths (e.g., sentences in n…
CERT-ED: Certifiably Robust Text Classification for Edit Distance
Zhuoqun Huang, Neil G Marchant, Olga Ohrimenko +1
With the growing integration of AI in daily life, ensuring the robustness of systems to inference-time attacks is crucial. Among the approaches for certifying robustness to such 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…
A Primer on the Data Cleaning Pipeline
Rebecca C. Steorts
The availability of both structured and unstructured databases, such as electronic health data, social media data, patent data, and surveys that are often updated in real time, amo…
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
Zhuoqun Huang, Neil G. Marchant, Keane Lucas +3
Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused…
Bayesian Graphical Entity Resolution Using Exchangeable Random Partition Priors
Neil G. Marchant, Benjamin I. P. Rubinstein, Rebecca C. Steorts
Entity resolution (record linkage or deduplication) is the process of identifying and linking duplicate records in databases. In this paper, we propose a Bayesian graphical approac…
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…
d-blink: Distributed End-to-End Bayesian Entity Resolution
Neil G. Marchant, Andee Kaplan, Daniel N. Elazar +2
Entity resolution (ER; also known as record linkage or de-duplication) is the process of merging noisy databases, often in the absence of unique identifiers. A major advancement in…
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…