We introduce the problem of active causal structure learning with advice. In the typical well-
studied setting, the learning algorithm is given the essential graph for the observational …

We introduce a novel measure for quantifying the error in input predictions. The error is
based on a minimum-cost hyperedge cover in a suitably defined hypergraph and provides a …

Abstract The Capacitated Team Orienteering Problem (CTOP) is a challenging
combinatorial optimization problem, wherein a fleet of vehicles traverses multiple locations …

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 …

We study the Online Traveling Salesperson Problem (OLTSP) with predictions. In OLTSP, a
sequence of initially unknown requests arrive over time at points (locations) of a metric …

We study the problem of online unweighted bipartite matching with $ n $ offline vertices and
$ n $ online vertices where one wishes to be competitive against the optimal offline …

We revisit the problem of distribution learning within the framework of learning-augmented
algorithms. In this setting, we explore the scenario where a probability distribution is …

In the Time-Windows TSP (TW-TSP) we are given requests at different locations on a
network; each request is endowed with a reward and an interval of time; the goal is to find a …

We revisit the classical non-clairvoyant problem of scheduling a set of n jobs on a set of m
parallel identical machines where the processing time of a job is not known until the job …

In this paper, we consider the Online Traveling Salesperson Problem (OLTSP) where the
locations of the requests are known in advance, but not their arrival times. We study both the …