937 citations · 3.8k across the 80 of their papers we have counts for
21 papers · 1 filter
Accelerated, Optimal, and Parallel: Some Results on Model-Based Stochastic Optimization
Karan Chadha, Gary Cheng, John C. Duchi
We extend the Approximate-Proximal Point (aProx) family of model-based methods for solving stochastic convex optimization problems, including stochastic subgradient, proximal point…
Large-Scale Methods for Distributionally Robust Optimization
Daniel Levy, Yair Carmon, John C. Duchi +1
We propose and analyze algorithms for distributionally robust optimization of convex losses with conditional value at risk (CVaR) and divergence uncertainty sets. We prove th…
First-Order Methods for Nonconvex Quadratic Minimization
Yair Carmon, John C. Duchi
We consider minimization of indefinite quadratics with either trust-region (norm) constraints or cubic regularization. Despite the nonconvexity of these problems we prove that, und…
Lower Bounds for Non-Convex Stochastic Optimization
Yossi Arjevani, Yair Carmon, John C. Duchi +3
We lower bound the complexity of finding -stationary points (with gradient norm at most ) using stochastic first-order methods. In a well-studied model where algorithms acces…
Geometry, Computation, and Optimality in Stochastic Optimization
Chen Cheng, Daniel Levy, John C. Duchi
We study computational and statistical consequences of problem geometry in stochastic and online optimization. By focusing on constraint set and gradient geometry, we characterize…
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…