online convex optimization
Online convex optimization deals with problems where decisions are made sequentially based on incoming data streams, optimizing a convex loss function while only having access to partial information at each step.
- $O(\sqrt{T})$ Static Regret and Instance Dependent Constraint Violation for Constrained Online Convex Optimization
- An Ellipsoid Algorithm for Online Convex Optimization
- Beyond $\tilde{O}(\sqrt{T})$ Constraint Violation for Online Convex Optimization with Adversarial Constraints
- Dynamic Regret Reduces to Kernelized Static Regret
- Non-stationary Bandit Convex Optimization: A Comprehensive Study
- On the necessity of adaptive regularisation: Optimal anytime online learning on $\boldsymbol{\ell_p}$-balls
- Parameter-free Algorithms for the Stochastically Extended Adversarial Model