2 citations · 3 across the 6 of their papers we have counts for
6 papers
Achieving Distributive Justice in Federated Learning via Uncertainty Quantification
Alycia Carey, Xintao Wu
Client-level fairness metrics for federated learning are used to ensure that all clients in a federation either: a) have similar final performance on their local data distributions…
DP-TabICL: In-Context Learning with Differentially Private Tabular Data
Alycia N. Carey, Karuna Bhaila, Kennedy Edemacu +1
In-context learning (ICL) enables large language models (LLMs) to adapt to new tasks by conditioning on demonstrations of question-answer pairs and it has been shown to have compar…
Robust Influence-based Training Methods for Noisy Brain MRI
Minh-Hao Van, Alycia N. Carey, Xintao Wu
Correctly classifying brain tumors is imperative to the prompt and accurate treatment of a patient. While several classification algorithms based on classical image processing or d…
Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
Alycia N. Carey, Minh-Hao Van, Xintao Wu
How to properly set the privacy parameter in differential privacy (DP) has been an open question in DP research since it was first proposed in 2006. In this work, we demonstrate th…
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
Minh-Hao Van, Alycia N. Carey, Xintao Wu
While numerous defense methods have been proposed to prohibit potential poisoning attacks from untrusted data sources, most research works only defend against specific attacks, whi…
The Fairness Field Guide: Perspectives from Social and Formal Sciences
Alycia N. Carey, Xintao Wu
Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed. However, despite the growing number of publications…