90 citations · 149 across the 19 of their papers we have counts for
7 papers · 1 filter
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…
Adaptation and Generalization for Unknown Sensitive Factors of Variations
William Paul, Philippe Burlina
Assured AI in unrestricted settings is a critical problem. Our framework addresses AI assurance challenges lying at the intersection of domain adaptation, fairness, and counterfact…
TARA: Training and Representation Alteration for AI Fairness and Domain Generalization
William Paul, Armin Hadzic, Neil Joshi +2
We propose a novel method for enforcing AI fairness with respect to protected or sensitive factors. This method uses a dual strategy performing training and representation alterati…
Least th-Order and Rényi Generative Adversarial Networks
Himesh Bhatia, William Paul, Fady Alajaji +2
We investigate the use of parametrized families of information-theoretic measures to generalize the loss functions of generative adversarial networks (GANs) with the objective of i…
Occupancy Map Prediction Using Generative and Fully Convolutional Networks for Vehicle Navigation
Kapil Katyal, Katie Popek, Chris Paxton +4
Fast, collision-free motion through unknown environments remains a challenging problem for robotic systems. In these situations, the robot's ability to reason about its future moti…