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Found 5 papers, 1 papers with code
Robust Influence-based Training Methods for Noisy Brain MRI
In this work, we study a difficult but realistic setting of training a deep learning model on noisy MR images to classify brain tumors.
DP-TabICL: In-Context Learning with Differentially Private Tabular Data
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 comparable performance to costly model retraining and fine-tuning.
HINT: Healthy Influential-Noise based Training to Defend against Data Poisoning Attacks
While numerous defense methods have been proposed to prohibit potential poisoning attacks from untrusted data sources, most research works only defend against specific attacks, which leaves many avenues for an adversary to exploit.
Evaluating the Impact of Local Differential Privacy on Utility Loss via Influence Functions
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.
The Fairness Field Guide: Perspectives from Social and Formal Sciences
Over the past several years, a slew of different methods to measure the fairness of a machine learning model have been proposed.
