Test Score Algorithms for Budgeted Stochastic Utility Maximization

@article{Lee2020TestSA,
  title={Test Score Algorithms for Budgeted Stochastic Utility Maximization},
  author={Dabeen Lee and Milan Vojnovi{\'c} and Seyoung Yun},
  journal={ArXiv},
  year={2020},
  volume={abs/2012.15194}
}
Motivated by recent developments in designing algorithms based on individual item scores for solving utility maximization problems, we study the framework of using test scores, defined as a statistic of observed individual item performance data, for solving the budgeted stochastic utility maximization problem. We extend an existing scoring mechanism, namely, the replication test scores, to incorporate heterogeneous item costs as well as item values. We show that a natural greedy algorithm that… 
[PDF] Semantic Reader
1 Citations
Background Citations
1

One Citation

Sketching stochastic valuation functions

The problem of sketching a stochastic valuation function is considered as the expectation of a valuation function of independent random item values, and it is shown that for monotone subadditive or submodular valuation functions that satisfy a weak homogeneity condition, or certain other conditions, there exist discretized distributions of item values that yield a sketch valuation function which is a constant-factor approximation.

References

SHOWING 1-10 OF 72 REFERENCES

Team Performance with Test Scores

These new tests measure the “potential” of individuals, in a precise sense, rather than performance, and represent the first time that individual tests have been shown to produce near-optimal teams for a nontrivial team performance measure.

A Simple 0.5-Bounded Greedy Algorithm for the 0/1 Knapsack Problem

Streaming Algorithms for Maximizing Monotone Submodular Functions Under a Knapsack Constraint

This paper considers the problem of maximizing a monotone submodular function subject to a knapsack constraint in the streaming setting, and proposes a newroximation algorithm, requiring only a single pass through the data.

Fast algorithms for maximizing submodular functions

A new variant of the continuous greedy algorithm, which interpolates between the classical greedy algorithm and a truly continuous algorithm, is developed, which can be implemented for matroid and knapsack constraints using O(n2) oracle calls to the objective function.

Do Less, Get More: Streaming Submodular Maximization with Subsampling

This paper develops the first one-pass streaming algorithm for submodular maximization that does not evaluate the entire stream even once, and outperforms the state-of-the-art algorithm by up to fifty fold, while maintaining practically the same utility.

Submodular set functions, matroids and the greedy algorithm: Tight worst-case bounds and some generalizations of the Rado-Edmonds theorem

Submodularity in Data Subset Selection and Active Learning

The connection of submodularity to the data likelihood functions for Naive Bayes and Nearest Neighbor classifiers is shown, and the data subset selection problems for these classifiers are formulated as constrained submodular maximization.

Streaming submodular maximization: massive data summarization on the fly

This paper develops the first efficient streaming algorithm with constant factor 1/2-ε approximation guarantee to the optimum solution, requiring only a single pass through the data, and memory independent of data size.

Maximizing Stochastic Monotone Submodular Functions

This model can capture the effect of stochasticity in a wide range of applications and shows that the adaptivity gap—the ratio between the values of optimal adaptive and optimal nonadaptive policies—is bounded.

Cost-effective outbreak detection in networks

This work exploits submodularity to develop an efficient algorithm that scales to large problems, achieving near optimal placements, while being 700 times faster than a simple greedy algorithm and achieving speedups and savings in storage of several orders of magnitude.
...