We consider the question of speeding up classic graph algorithms with machine-learned
predictions. In this model, algorithms are furnished with extra advice learned from past or …

Several problems have been extensively studied in the learning-augmented setting, where
the algorithm has access to some, possibly incorrect, predictions. However, it is assumed in …

We provide the first sub-linear space and sub-linear regret algorithm for online learning with
expert advice (against an oblivious adversary), addressing an open question raised recently …

The research area of algorithms with predictions has seen recent success showing how to
incorporate machine learning into algorithm design to improve performance when the …

The online linear optimization paradigm is important to many real-world network
applications as well as theoretical algorithmic studies. Recent studies have made attempts …

A variant of the online knapsack problem is considered in the settings of trusted and
untrusted predictions. In Unit Profit Knapsack, the items have unit profit, and it is easy to find …

The secretary problem is one of the fundamental problems in online decision making; a tight
competitive ratio for this problem of $1/\mathrm {e}\approx 0.368$ has been known since the …

Let F be a fixed finite obstruction set of graphs and G be a graph revealed in an online
fashion, node by node. The online Delayed F-Node-Deletion Problem (F-Edge-Deletion …

A variant of the online knapsack problem is considered in the setting of predictions. In Unit
Profit Knapsack, the items have unit profit, ie, the goal is to pack as many items as possible …

Push-Relabel is one of the most celebrated network flow algorithms. Maintaining a pre-flow
that saturates a cut, it enjoys better theoretical and empirical running time than other flow …