9 citations · 15 across the 7 of their papers we have counts for
16 papers
Addressing Heterogeneity in Federated Learning via Distributional Transformation
Haolin Yuan, Bo Hui, Yuchen Yang +3
Federated learning (FL) allows multiple clients to collaboratively train a deep learning model. One major challenge of FL is when data distribution is heterogeneous, i.e., differs…
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…
EdgeMixup: Improving Fairness for Skin Disease Classification and Segmentation
Haolin Yuan, Armin Hadzic, William Paul +5
Skin lesions can be an early indicator of a wide range of infectious and other diseases. The use of deep learning (DL) models to diagnose skin lesions has great potential in assist…
Patch Attack Invariance: How Sensitive are Patch Attacks to 3D Pose?
Max Lennon, Nathan Drenkow, Philippe Burlina
Perturbation-based attacks, while not physically realizable, have been the main emphasis of adversarial machine learning (ML) research. Patch-based attacks by contrast are physical…
AI Fairness via Domain Adaptation
Neil Joshi, Phil Burlina
While deep learning (DL) approaches are reaching human-level performance for many tasks, including for diagnostics AI, the focus is now on challenges possibly affecting DL deployme…