53 citations · 178 across the 24 of their papers we have counts for
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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…
Accelerated Parameter-Free Stochastic Optimization
Itai Kreisler, Maor Ivgi, Oliver Hinder +1
We propose a method that achieves near-optimal rates for smooth stochastic convex optimization and requires essentially no prior knowledge of problem parameters. This improves on p…
Datasets and Benchmarks for Nanophotonic Structure and Parametric Design Simulations
Jungtaek Kim, Mingxuan Li, Oliver Hinder +1
Nanophotonic structures have versatile applications including solar cells, anti-reflective coatings, electromagnetic interference shielding, optical filters, and light emitting dio…
DoG is SGD's Best Friend: A Parameter-Free Dynamic Step Size Schedule
Maor Ivgi, Oliver Hinder, Yair Carmon
We propose a tuning-free dynamic SGD step size formula, which we call Distance over Gradients (DoG). The DoG step sizes depend on simple empirical quantities (distance from the ini…