Online metric algorithms with untrusted predictions
Machine-learned predictors, although achieving very good results for inputs resembling
training data, cannot possibly provide perfect predictions in all situations. Still, decision …
training data, cannot possibly provide perfect predictions in all situations. Still, decision …
Prophet secretary for combinatorial auctions and matroids
The secretary and the prophet inequality problems are central to the field of Stop**
Theory. Recently, there has been a lot of work in generalizing these models to multiple items …
Theory. Recently, there has been a lot of work in generalizing these models to multiple items …
Optimal robustness-consistency tradeoffs for learning-augmented metrical task systems
We examine the problem of designing learning-augmented algorithms for metrical task
systems (MTS) that exploit machine-learned advice while maintaining rigorous, worst-case …
systems (MTS) that exploit machine-learned advice while maintaining rigorous, worst-case …
A universal error measure for input predictions applied to online graph problems
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 …
based on a minimum-cost hyperedge cover in a suitably defined hypergraph and provides a …
The online 𝑘-taxi problem
We consider the online k-taxi problem, a generalization of the k-server problem, in which k
taxis serve a sequence of requests in a metric space. A request consists of two points s and …
taxis serve a sequence of requests in a metric space. A request consists of two points s and …
Random order online set cover is as easy as offline
We give a polynomial-time algorithm for Online-SetCover with a competitive ratio of
O(\logmn) when the elements are revealed in random order, matching the best possible …
O(\logmn) when the elements are revealed in random order, matching the best possible …
Efficient online clustering with moving costs
In this work we consider an online learning problem, called Online $ k $-Clustering with
Moving Costs, at which a learner maintains a set of $ k $ facilities over $ T $ rounds so as to …
Moving Costs, at which a learner maintains a set of $ k $ facilities over $ T $ rounds so as to …
Stochastic online metric matching
We study the minimum-cost metric perfect matching problem under online iid arrivals. We
are given a fixed metric with a server at each of the points, and then requests arrive online …
are given a fixed metric with a server at each of the points, and then requests arrive online …
Set covering with our eyes wide shut
In the stochastic set cover problem (Grandoni et al., FOCS '08), we are given a collection S
of m sets over a universe U of size N, and a distribution D over elements of U. The algorithm …
of m sets over a universe U of size N, and a distribution D over elements of U. The algorithm …