206 citations · 808 across the 40 of their papers we have counts for
4 papers · 1 filter
Accuracy on the Line: On the Strong Correlation Between Out-of-Distribution and In-Distribution Generalization
John Miller, Rohan Taori, Aditi Raghunathan +6
For machine learning systems to be reliable, we must understand their performance in unseen, out-of-distribution environments. In this paper, we empirically show that out-of-distri…
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…