206 citations · 808 across the 40 of their papers we have counts for
46 papers
Clipping the Price of Adaptivity at the Tail
Itai Kreisler, Yair Carmon, Oliver Hinder
Adaptive stochastic convex optimization (SCO) methods face a fundamental ``price of adaptivity'' barrier: under the standard set of assumptions, they cannot efficiently adapt to la…
The Sample Complexity of Parameter-Free Stochastic Convex Optimization
Jared Lawrence, Ari Kalinsky, Hannah Bradfield +2
We study the sample complexity of stochastic convex optimization when problem parameters such as the distance to optimality and the Lipschitz constant are unknown. We pursue two st…
An Analytical Model for Overparameterized Learning Under Class Imbalance
Eliav Mor, Yair Carmon
We study class-imbalanced linear classification in a high-dimensional Gaussian mixture model. We develop a tight, closed form approximation for the test error of several practical…
Filter Like You Test: Data-Driven Data Filtering for CLIP Pretraining
Mikey Shechter, Yair Carmon
We introduce Filter Like You Test (FLYT), an algorithm for curating large-scale vision-language datasets that learns the usefulness of each data point as a pretraining example. FLY…
Convergence of Clipped SGD on Convex -Smooth Functions
Ofir Gaash, Kfir Yehuda Levy, Yair Carmon
We study stochastic gradient descent (SGD) with gradient clipping on convex functions under a generalized smoothness assumption called -smoothness. Using gradient clippi…
Extracting Dual Solutions via Primal Optimizers
Yair Carmon, Arun Jambulapati, Liam O'Carroll +1
We provide a general method to convert a "primal" black-box algorithm for solving regularized convex-concave minimax optimization problems into an algorithm for solving the associa…