1.9k citations · 2.8k across the 14 of their papers we have counts for
14 papers
Training Differentially Private Ad Prediction Models with Semi-Sensitive Features
Lynn Chua, Qiliang Cui, Badih Ghazi +9
Motivated by problems arising in digital advertising, we introduce the task of training differentially private (DP) machine learning models with semi-sensitive features. In this se…
Optimal Unbiased Randomizers for Regression with Label Differential Privacy
Ashwinkumar Badanidiyuru, Badih Ghazi, Pritish Kamath +5
We propose a new family of label randomizers for training regression models under the constraint of label differential privacy (DP). In particular, we leverage the trade-offs betwe…
Summary Reports Optimization in the Privacy Sandbox Attribution Reporting API
Hidayet Aksu, Badih Ghazi, Pritish Kamath +4
The Privacy Sandbox Attribution Reporting API has been recently deployed by Google Chrome to support the basic advertising functionality of attribution reporting (aka conversion me…
Regression with Label Differential Privacy
Badih Ghazi, Pritish Kamath, Ravi Kumar +4
We study the task of training regression models with the guarantee of label differential privacy (DP). Based on a global prior distribution on label values, which could be obtained…
Private Ad Modeling with DP-SGD
Carson Denison, Badih Ghazi, Pritish Kamath +6
A well-known algorithm in privacy-preserving ML is differentially private stochastic gradient descent (DP-SGD). While this algorithm has been evaluated on text and image data, it h…
Discovering novel systemic biomarkers in photos of the external eye
Boris Babenko, Ilana Traynis, Christina Chen +16
External eye photos were recently shown to reveal signs of diabetic retinal disease and elevated HbA1c. In this paper, we evaluate if external eye photos contain information about…
Detecting hidden signs of diabetes in external eye photographs
Boris Babenko, Akinori Mitani, Ilana Traynis +10
Diabetes-related retinal conditions can be detected by examining the posterior of the eye. By contrast, examining the anterior of the eye can reveal conditions affecting the front…
Predicting Risk of Developing Diabetic Retinopathy using Deep Learning
Ashish Bora, Siva Balasubramanian, Boris Babenko +13
Diabetic retinopathy (DR) screening is instrumental in preventing blindness, but faces a scaling challenge as the number of diabetic patients rises. Risk stratification for the dev…
Scientific Discovery by Generating Counterfactuals using Image Translation
Arunachalam Narayanaswamy, Subhashini Venugopalan, Dale R. Webster +10
Model explanation techniques play a critical role in understanding the source of a model's performance and making its decisions transparent. Here we investigate if explanation tech…
Detecting Anemia from Retinal Fundus Images
Akinori Mitani, Yun Liu, Abigail Huang +5
Despite its high prevalence, anemia is often undetected due to the invasiveness and cost of screening and diagnostic tests. Though some non-invasive approaches have been developed,…
Predicting Progression of Age-related Macular Degeneration from Fundus Images using Deep Learning
Boris Babenko, Siva Balasubramanian, Katy E. Blumer +5
Background: Patients with neovascular age-related macular degeneration (AMD) can avoid vision loss via certain therapy. However, methods to predict the progression to neovascular a…
Predicting optical coherence tomography-derived diabetic macular edema grades from fundus photographs using deep learning
Avinash Varadarajan, Pinal Bavishi, Paisan Raumviboonsuk +15
Diabetic eye disease is one of the fastest growing causes of preventable blindness. With the advent of anti-VEGF (vascular endothelial growth factor) therapies, it has become incre…
Deep learning for predicting refractive error from retinal fundus images
Avinash V. Varadarajan, Ryan Poplin, Katy Blumer +7
Refractive error, one of the leading cause of visual impairment, can be corrected by simple interventions like prescribing eyeglasses. We trained a deep learning algorithm to predi…
Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning
Ryan Poplin, Avinash V. Varadarajan, Katy Blumer +5
Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses. However, observing and quantifying associations in im…