no code implementations • 18 Feb 2024 • Ruicheng Ao, Hongyu Chen, David Simchi-Levi, Feng Zhu
We start with general arrival distributions and show that a simple policy achieves a $O(\sqrt{T})$ regret.
no code implementations • 16 Nov 2022 • Ruicheng Ao, Shicong Cen, Yuejie Chi
Moving beyond, we demonstrate entropy-regularized OMWU -- by adopting two-timescale learning rates in a delay-aware manner -- enjoys faster last-iterate convergence under fixed delays, and continues to converge provably even when the delays are arbitrarily bounded in an average-iterate manner.
no code implementations • 15 Jul 2022 • Jiang Hu, Ruicheng Ao, Anthony Man-Cho So, MingHan Yang, Zaiwen Wen
Moreover, we show that if the loss function satisfies certain convexity and smoothness conditions and the input-output map satisfies a Riemannian Jacobian stability condition, then our proposed method enjoys a local linear -- or, under the Lipschitz continuity of the Riemannian Jacobian of the input-output map, even quadratic -- rate of convergence.
