30 citations · 102 across the 35 of their papers we have counts for
3 papers · 1 filter
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…
Unconstrained Dynamic Regret via Sparse Coding
Zhiyu Zhang, Ashok Cutkosky, Ioannis Ch. Paschalidis
Motivated by the challenge of nonstationarity in sequential decision making, we study Online Convex Optimization (OCO) under the coupling of two problem structures: the domain is u…