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Found 5 papers, 0 papers with code
Algorithms that Approximate Data Removal: New Results and Limitations
We study the problem of deleting user data from machine learning models trained using empirical risk minimization.
When Personalization Harms: Reconsidering the Use of Group Attributes in Prediction
In this work, we show models that are personalized with group attributes can reduce performance at a group level.
Public Data-Assisted Mirror Descent for Private Model Training
In this paper, we revisit the problem of using in-distribution public data to improve the privacy/utility trade-offs for differentially private (DP) model training.
Chasing Your Long Tails: Differentially Private Prediction in Health Care Settings
Our results highlight lesser-known limitations of methods for DP learning in health care, models that exhibit steep tradeoffs between privacy and utility, and models whose predictions are disproportionately influenced by large demographic groups in the training data.
Using Generative Models for Pediatric wbMRI
Early detection of cancer is key to a good prognosis and requires frequent testing, especially in pediatrics.
