59 citations · 111 across the 12 of their papers we have counts for
8 papers · 1 filter
Training Private Models That Know What They Don't Know
Stephan Rabanser, Anvith Thudi, Abhradeep Thakurta +2
Training reliable deep learning models which avoid making overconfident but incorrect predictions is a longstanding challenge. This challenge is further exacerbated when learning h…
Faster Differentially Private Convex Optimization via Second-Order Methods
Arun Ganesh, Mahdi Haghifam, Thomas Steinke +1
Differentially private (stochastic) gradient descent is the workhorse of DP private machine learning in both the convex and non-convex settings. Without privacy constraints, second…
Private (Stochastic) Non-Convex Optimization Revisited: Second-Order Stationary Points and Excess Risks
Arun Ganesh, Daogao Liu, Sewoong Oh +1
We consider the problem of minimizing a non-convex objective while preserving the privacy of the examples in the training data. Building upon the previous variance-reduced algorith…
Why Is Public Pretraining Necessary for Private Model Training?
Arun Ganesh, Mahdi Haghifam, Milad Nasr +5
In the privacy-utility tradeoff of a model trained on benchmark language and vision tasks, remarkable improvements have been widely reported with the use of pretraining on publicly…
Multi-Task Differential Privacy Under Distribution Skew
Walid Krichene, Prateek Jain, Shuang Song +3
We study the problem of multi-task learning under user-level differential privacy, in which users contribute data to tasks, each involving a subset of users. One important…
(Nearly) Optimal Private Linear Regression via Adaptive Clipping
Prateek Varshney, Abhradeep Thakurta, Prateek Jain
We study the problem of differentially private linear regression where each data point is sampled from a fixed sub-Gaussian style distribution. We propose and analyze a one-pass mi…