Traditional online algorithms encapsulate decision making under uncertainty, and give ways
to hedge against all possible future events, while guaranteeing a nearly optimal solution, as …

Machine-learned predictors, although achieving very good results for inputs resembling
training data, cannot possibly provide perfect predictions in all situations. Still, decision …

We explore the fundamental problem of sorting through the lens of learning-augmented
algorithms, where algorithms can leverage possibly erroneous predictions to improve their …

We study the problem of improving the performance of online algorithms by incorporating
machine-learned predictions. The goal is to design algorithms that are both consistent and …

We propose a new model for augmenting algorithms with predictions by requiring that they
are formally learnable and instance robust. Learnability ensures that predictions can be …

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 …

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 …

Estimating frequencies of elements appearing in a data stream is a key task in large-scale
data analysis. Popular sketching approaches to this problem (eg, CountMin and …

We study the problem of designing online algorithms given advice about the input. While
prior work had focused on deterministic advice, we only assume distributional access to the …

Exploring unknown environments is a fundamental task in many domains, eg, robot
navigation, network security, and internet search. We initiate the study of a learning …