TY  - GEN
UR  - https://proceedings.mlr.press/v132/pasteris21a.html
Y1  - 2021/01/01/
N2  - In this paper, we provide a rigorous theoretical investigation of an online learning version of the Facility Location problem which is motivated by emerging problems in real-world applications. In our formulation, we are given a set of sites and an online sequence of user requests. At each trial, the learner selects a subset of sites and then incurs a cost for each selected site and an additional cost which is the price of the user?s connection to the nearest site in the selected subset. The problem may be solved by an application of the well-known Hedge algorithm. This would, however, require time and space exponential in the number of the given sites, which motivates our design of a novel quasi-linear time algorithm for this problem, with good theoretical guarantees on its performance.
PB  - PMLR 132
TI  - Online Learning of Facility Locations
N1  - This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ID  - discovery10172868
AV  - public
A1  - Pasteris, S
A1  - He, T
A1  - Vitale, F
A1  - Wang, S
A1  - Herbster, M
SP  - 1002
SN  - 2640-3498
EP  - 1050
KW  - Machine learning
KW  -  Online optimisation
KW  -  Facility location problem
ER  -