30 citations · 102 across the 35 of their papers we have counts for
36 papers
Stochastic Inertial Krasnosel'skii-Mann Iteration Achieves Near-Optimal Sample Complexity
Tong Yang, Tao Jiang, Yuejie Chi +2
We analyze a simple stochastic inertial Krasnosel'skii--Mann (iKM) method for finding a fixed point of a nonexpansive operator in a real Hilbert space. Our method is obtained simpl…
Unconstrained Robust Online Convex Optimization
Jiujia Zhang, Ashok Cutkosky
This paper addresses online learning with ``corrupted'' feedback. Our learner is provided with potentially corrupted gradients instead of the ``true'' gradients .…
Fully Unconstrained Online Learning
Ashok Cutkosky, Zakaria Mhammedi
We provide an online learning algorithm that obtains regret on -Lipschitz convex losses for any comparison…
Online Linear Regression in Dynamic Environments via Discounting
Andrew Jacobsen, Ashok Cutkosky
We develop algorithms for online linear regression which achieve optimal static and dynamic regret guarantees \emph{even in the complete absence of prior knowledge}. We present a n…
Improving Adaptive Online Learning Using Refined Discretization
Zhiyu Zhang, Heng Yang, Ashok Cutkosky +1
We study unconstrained Online Linear Optimization with Lipschitz losses. Motivated by the pursuit of instance optimality, we propose a new algorithm that simultaneously achieves ($…
Unconstrained Online Learning with Unbounded Losses
Andrew Jacobsen, Ashok Cutkosky
Algorithms for online learning typically require one or more boundedness assumptions: that the domain is bounded, that the losses are Lipschitz, or both. In this paper, we develop…