74 citations · 172 across the 28 of their papers we have counts for
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math.OC2021
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…
math.OC2019★ 50 cited
The importance of better models in stochastic optimization
Hilal Asi, John C. Duchi
Standard stochastic optimization methods are brittle, sensitive to stepsize choices and other algorithmic parameters, and they exhibit instability outside of well-behaved families…
math.OC2018★ 74 cited
Stochastic (Approximate) Proximal Point Methods: Convergence, Optimality, and Adaptivity
Hilal Asi, John C. Duchi
We develop model-based methods for solving stochastic convex optimization problems, introducing the approximate-proximal point, or aProx, family, which includes stochastic subgradi…