5 papers
Predictability as a Fine-Grained Measure for Privacy
Linda Lu, Karthik Sridharan
Differential privacy (DP) ensures rigorous individual-level privacy guarantees against even the most knowledgeable attackers, but its worst-case nature can impose a costly privacy-…
Transfer Learning from One Cancer to Another via Deep Learning Domain Adaptation
Justin Cheung, Samuel Savine, Calvin Nguyen +2
Supervised deep learning models often achieve excellent performance within their training distribution but struggle to generalize beyond it. In cancer histopathology, for example,…
Neural Collapse in Test-Time Adaptation
Xiao Chen, Zhongjing Du, Jiazhen Huang +4
Test-Time Adaptation (TTA) enhances model robustness to out-of-distribution (OOD) data by updating the model online during inference, yet existing methods lack theoretical insights…
Improving Artifact Robustness for CT Deep Learning Models Without Labeled Artifact Images via Domain Adaptation
Justin Cheung, Samuel Savine, Calvin Nguyen +2
If a CT scanner introduces a new artifact not present in the training labels, the model may misclassify the images. Although modern CT scanners include design features which mitiga…
System-Aware Unlearning Algorithms: Use Lesser, Forget Faster
Linda Lu, Ayush Sekhari, Karthik Sridharan
Machine unlearning addresses the problem of updating a machine learning model/system trained on a dataset so that the influence of a set of deletion requests on…