2 citations · 5 across the 4 of their papers we have counts for
4 papers
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…
How Compression and Approximation Affect Efficiency in String Distance Measures
Arun Ganesh, Tomasz Kociumaka, Andrea Lincoln +1
Real-world data often comes in compressed form. Analyzing compressed data directly (without decompressing it) can save space and time by orders of magnitude. In this work, we focus…