Search Results for author:
Found 8 papers, 4 papers with code
Private Multi-Winner Voting for Machine Learning
We use our mechanisms to enable privacy-preserving multi-label learning in the central setting by extending the canonical single-label technique: PATE.
On the Fundamental Limits of Formally (Dis)Proving Robustness in Proof-of-Learning
We contribute a formal analysis of why the PoL protocol cannot be formally (dis)proven to be robust against spoofing adversaries.
Is Fairness Only Metric Deep? Evaluating and Addressing Subgroup Gaps in Deep Metric Learning
Deep metric learning (DML) enables learning with less supervision through its emphasis on the similarity structure of representations.
Improving the Fairness of Chest X-ray Classifiers
We also find that methods which achieve group fairness do so by worsening performance for all groups.
Reading Race: AI Recognises Patient's Racial Identity In Medical Images
Methods: Using private and public datasets we evaluate: A) performance quantification of deep learning models to detect race from medical images, including the ability of these models to generalize to external environments and across multiple imaging modalities, B) assessment of possible confounding anatomic and phenotype population features, such as disease distribution and body habitus as predictors of race, and C) investigation into the underlying mechanism by which AI models can recognize race.
An Empirical Framework for Domain Generalization in Clinical Settings
In this work, we benchmark the performance of eight domain generalization methods on multi-site clinical time series and medical imaging data.
Proof-of-Learning: Definitions and Practice
In particular, our analyses and experiments show that an adversary seeking to illegitimately manufacture a proof-of-learning needs to perform *at least* as much work than is needed for gradient descent itself.
CaPC Learning: Confidential and Private Collaborative Learning
There is currently no method that enables machine learning in such a setting, where both confidentiality and privacy need to be preserved, to prevent both explicit and implicit sharing of data.
