30 citations · 38 across the 6 of their papers we have counts for
11 papers · 1 filter
Distributionally Robust Optimization via Ball Oracle Acceleration
Yair Carmon, Danielle Hausler
We develop and analyze algorithms for distributionally robust optimization (DRO) of convex losses. In particular, we consider group-structured and bounded -divergence uncertaint…
Never Go Full Batch (in Stochastic Convex Optimization)
Idan Amir, Yair Carmon, Tomer Koren +1
We study the generalization performance of optimization algorithms for stochastic convex optimization: these are first-order methods that only access the exact…
Stochastic Bias-Reduced Gradient Methods
Hilal Asi, Yair Carmon, Arun Jambulapati +2
We develop a new primitive for stochastic optimization: a low-bias, low-cost estimator of the minimizer of any Lipschitz strongly-convex function. In particular, we use a…
Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss
Yair Carmon, Arun Jambulapati, Yujia Jin +1
We characterize the complexity of minimizing for convex, Lipschitz functions . For non-smooth functions, existing methods require $O(Nε^{-2…
Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy, Yair Carmon, John C. Duchi +1
We propose and analyze algorithms for distributionally robust optimization of convex losses with conditional value at risk (CVaR) and divergence uncertainty sets. We prove th…
Acceleration with a Ball Optimization Oracle
Yair Carmon, Arun Jambulapati, Qijia Jiang +4
Consider an oracle which takes a point and returns the minimizer of a convex function in an ball of radius around . It is straightforward to show that rough…