Search Results for author:
Found 5 papers, 1 papers with code
Higher-order methods for convex-concave min-max optimization and monotone variational inequalities
We provide improved convergence rates for constrained convex-concave min-max problems and monotone variational inequalities with higher-order smoothness.
Last-iterate convergence rates for min-max optimization
While classic work in convex-concave min-max optimization relies on average-iterate convergence results, the emergence of nonconvex applications such as training Generative Adversarial Networks has led to renewed interest in last-iterate convergence guarantees.
Faster Rates for Convex-Concave Games
We consider the use of no-regret algorithms to compute equilibria for particular classes of convex-concave games.
Agnostic Estimation of Mean and Covariance
We consider the problem of estimating the mean and covariance of a distribution from iid samples in $\mathbb{R}^n$, in the presence of an $\eta$ fraction of malicious noise; this is in contrast to much recent work where the noise itself is assumed to be from a distribution of known type.
Label optimal regret bounds for online local learning
We resolve an open question from (Christiano, 2014b) posed in COLT'14 regarding the optimal dependency of the regret achievable for online local learning on the size of the label set.
