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Chasing Convex Functions with Long-term Constraints
We introduce and study a family of online metric problems with long-term constraints.
Online Conversion with Switching Costs: Robust and Learning-Augmented Algorithms
We introduce competitive (robust) threshold-based algorithms for both the minimization and maximization variants of this problem, and show they are optimal among deterministic online algorithms.
Online Algorithms with Uncertainty-Quantified Predictions
In particular, we consider predictions augmented with uncertainty quantification describing the likelihood of the ground truth falling in a certain range, designing online algorithms with these probabilistic predictions for two classic online problems: ski rental and online search.
Chasing Convex Bodies and Functions with Black-Box Advice
We consider the problem of convex function chasing with black-box advice, where an online decision-maker aims to minimize the total cost of making and switching between decisions in a normed vector space, aided by black-box advice such as the decisions of a machine-learned algorithm.
