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 …

The online bin packing problem is a well-known optimization challenge that finds application
in a wide range of real-world scenarios. In the paper, we propose a novel algorithm called …

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

In this paper, we study learning-augmented algorithms for the Bahncard problem. The
Bahncard problem is a generalization of the ski-rental problem, where a traveler needs to …

The classical work of (Arora et al., 1999) provides a scheme that gives, for any $\epsilon>
0$, a polynomial time $1-\epsilon $ approximation algorithm for dense instances of a family …

ML-augmented algorithms utilize predictions to achieve performance beyond their worst-
case bounds. Producing these predictions might be a costly operation--this motivated Im et …

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 study the power of (competitive) algorithms with predictions in a multiagent setting. For
this we introduce a multiagent version of the ski-rental problem. In this problem agents can …

The classical work of (Arora et al., 1999) provides a scheme that gives, for any ϵ> 0, a
polynomial time 1− ϵ approximation algorithm for dense instances of a family of NP-hard …