Danielle Hausler — accepted AI-conference papers

2022

Distributionally Robust Optimization via Ball Oracle Acceleration

NeurIPS 2022accept

We develop and analyze algorithms for distributionally robust optimization (DRO) of convex losses. In particular, we consider group-structured and bounded $f$-divergence uncertainty sets. Our approach relies on an accelerated method that queries a ball optimization oracle, i.e., a subroutine that mi…

Cited by 18 Source ↗PDF ↗Scholar ↗

2022

Optimal and Adaptive Monteiro-Svaiter Acceleration

Yair Carmon, Danielle Hausler, Arun Jambulapati, Yujia Jin, Aaron Sidford

NeurIPS 2022accept

We develop a variant of the Monteiro-Svaiter (MS) acceleration framework that removes the need to solve an expensive implicit equation at every iteration. Consequently, for any $p\ge 2$ we improve the complexity of convex optimization with Lipschitz $p$th derivative by a logarithmic factor, matchin…

Cited by 37 Source ↗PDF ↗code ↗Scholar ↗