25 citations · 98 across the 22 of their papers we have counts for
4 papers · 1 filter
A Whole New Ball Game: A Primal Accelerated Method for Matrix Games and Minimizing the Maximum of Smooth Functions
Yair Carmon, Arun Jambulapati, Yujia Jin +1
We design algorithms for minimizing over a -dimensional Euclidean or simplex domain. When each is -Lipschitz and -smooth, our method computes…
Moments, Random Walks, and Limits for Spectrum Approximation
Yujia Jin, Christopher Musco, Aaron Sidford +1
We study lower bounds for the problem of approximating a one dimensional distribution given (noisy) measurements of its moments. We show that there are distributions on th…
Quantum Speedups for Zero-Sum Games via Improved Dynamic Gibbs Sampling
Adam Bouland, Yosheb Getachew, Yujia Jin +2
We give a quantum algorithm for computing an -approximate Nash equilibrium of a zero-sum game in a payoff matrix with bounded entries. Given a standard quantum orac…
ReSQueing Parallel and Private Stochastic Convex Optimization
Yair Carmon, Arun Jambulapati, Yujia Jin +4
We introduce a new tool for stochastic convex optimization (SCO): a Reweighted Stochastic Query (ReSQue) estimator for the gradient of a function convolved with a (Gaussian) probab…