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cs.LG2026
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…
cs.LG2026
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…