15 citations · 37 across the 14 of their papers we have counts for
14 papers
Trojans in Artificial Intelligence (TrojAI) Final Report
Kristopher W. Reese, Taylor Kulp-McDowall, Michael Majurski +68
The Intelligence Advanced Research Projects Activity (IARPA) launched the TrojAI program to confront an emerging vulnerability in modern artificial intelligence: the threat of AI T…
Synthetic-to-Real Domain Adaptation for Action Recognition: A Dataset and Baseline Performances
Arun V. Reddy, Ketul Shah, William Paul +6
Human action recognition is a challenging problem, particularly when there is high variability in factors such as subject appearance, backgrounds and viewpoint. While deep neural n…
Evaluating Trade-offs in Computer Vision Between Attribute Privacy, Fairness and Utility
William Paul, Philip Mathew, Fady Alajaji +1
This paper investigates to what degree and magnitude tradeoffs exist between utility, fairness and attribute privacy in computer vision. Regarding privacy, we look at this importan…
Classification Utility, Fairness, and Compactness via Tunable Information Bottleneck and Rényi Measures
Adam Gronowski, William Paul, Fady Alajaji +2
Designing machine learning algorithms that are accurate yet fair, not discriminating based on any sensitive attribute, is of paramount importance for society to accept AI for criti…
Renyi Fair Information Bottleneck for Image Classification
Adam Gronowski, William Paul, Fady Alajaji +2
We develop a novel method for ensuring fairness in machine learning which we term as the Renyi Fair Information Bottleneck (RFIB). We consider two different fairness constraints -…
Robustness and Adaptation to Hidden Factors of Variation
William Paul, Philippe Burlina
We tackle here a specific, still not widely addressed aspect, of AI robustness, which consists of seeking invariance / insensitivity of model performance to hidden factors of varia…