21 citations · 29 across the 5 of their papers we have counts for
3 papers · 1 filter
Privately Learning Mixtures of Axis-Aligned Gaussians
Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw
We consider the problem of learning mixtures of Gaussians under the constraint of approximate differential privacy. We prove that $\widetilde{O}(k^2 d \log^{3/2}(1/δ) / α^2 \vareps…
Simple and optimal high-probability bounds for strongly-convex stochastic gradient descent
Nicholas J. A. Harvey, Christopher Liaw, Sikander Randhawa
We consider stochastic gradient descent algorithms for minimizing a non-smooth, strongly-convex function. Several forms of this algorithm, including suffix averaging, are known to…
Tight Analyses for Non-Smooth Stochastic Gradient Descent
Nicholas J. A. Harvey, Christopher Liaw, Yaniv Plan +1
Consider the problem of minimizing functions that are Lipschitz and strongly convex, but not necessarily differentiable. We prove that after steps of stochastic gradient descen…