Name
Abstract | ||
|---|---|---|
We consider a problem wherein jobs arrive at random times and assume random values. Upon each job arrival, the decision-maker must decide immediately whether or not to accept the job and gain the value on offer as a reward, with the constraint that they may only accept at most $n$ jobs over some reference time period. The decision-maker only has access to $M$ independent realisations of the job arrival process. We propose an algorithm, Non-Parametric Sequential Allocation (NPSA), for solving this problem. Moreover, we prove that the expected reward returned by the NPSA algorithm converges in probability to optimality as $M$ grows large. We demonstrate the effectiveness of the algorithm empirically on synthetic data and on public fraud-detection datasets, from where the motivation for this work is derived. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.24963/ijcai.2021/579 | IJCAI |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Danial Dervovic | 1 | 0 | 0.34 |
| Parisa Hassanzadeh | 2 | 3 | 2.74 |
| Samuel A. Assefa | 3 | 0 | 1.69 |
| prashant reddy | 4 | 3 | 1.50 |