12 citations · 30 across the 8 of their papers we have counts for
8 papers
Differentially Private Modeling of Disease Transmission within Human Contact Networks
Shlomi Hod, Debanuj Nayak, Jason R. Gantenberg +3
Epidemiologic studies of infectious diseases often rely on models of contact networks to capture the complex interactions that govern disease spread, and ongoing projects aim to va…
Do You Really Need Public Data? Surrogate Public Data for Differential Privacy on Tabular Data
Shlomi Hod, Lucas Rosenblatt, Julia Stoyanovich
Differentially private (DP) machine learning often relies on the availability of public data for tasks like privacy-utility trade-off estimation, hyperparameter tuning, and pretrai…
ATTAXONOMY: Unpacking Differential Privacy Guarantees Against Practical Adversaries
Rachel Cummings, Shlomi Hod, Jayshree Sarathy +1
Differential Privacy (DP) is a mathematical framework that is increasingly deployed to mitigate privacy risks associated with machine learning and statistical analyses. Despite the…
Differentially Private Release of Israel's National Registry of Live Births
Shlomi Hod, Ran Canetti
In February 2024, Israel's Ministry of Health released microdata of live births in Israel in 2014. The dataset is based on Israel's National Registry of Live Births and offers subs…
Quantifying Local Specialization in Deep Neural Networks
Shlomi Hod, Daniel Filan, Stephen Casper +2
A neural network is locally specialized to the extent that parts of its computational graph (i.e. structure) can be abstractly represented as performing some comprehensible sub-tas…
Clusterability in Neural Networks
Daniel Filan, Stephen Casper, Shlomi Hod +3
The learned weights of a neural network have often been considered devoid of scrutable internal structure. In this paper, however, we look for structure in the form of clusterabili…
Performative Prediction in a Stateful World
Gavin Brown, Shlomi Hod, Iden Kalemaj
Deployed supervised machine learning models make predictions that interact with and influence the world. This phenomenon is called performative prediction by Perdomo et al. (ICML 2…
Pruned Neural Networks are Surprisingly Modular
Daniel Filan, Shlomi Hod, Cody Wild +2
The learned weights of a neural network are often considered devoid of scrutable internal structure. To discern structure in these weights, we introduce a measurable notion of modu…