21 citations · 39 across the 12 of their papers we have counts for
7 papers · 1 filter
Continuous Prediction with Experts' Advice
Victor Sanches Portella, Christopher Liaw, Nicholas J. A. Harvey
Prediction with experts' advice is one of the most fundamental problems in online learning and captures many of its technical challenges. A recent line of work has looked at online…
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…
Optimal anytime regret with two experts
Nicholas J. A. Harvey, Christopher Liaw, Edwin Perkins +1
We consider the classical problem of prediction with expert advice. In the fixed-time setting, where the time horizon is known in advance, algorithms that achieve the optimal regre…
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…
Near-optimal Sample Complexity Bounds for Robust Learning of Gaussians Mixtures via Compression Schemes
Hassan Ashtiani, Shai Ben-David, Nick Harvey +3
We prove that samples are necessary and sufficient for learning a mixture of Gaussians in , up to error in total va…