Tootfinder

This https://arxiv.org/abs/2303.09205 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Addressing bias in online selection with limited budget of comparisons
Consider a hiring process with candidates coming from different universities. It is easy to order candidates with the same background, yet it can be challenging to compare them otherwise. The latter case requires additional costly assessments, leading to a potentially high total cost for the hiring organization. Given an assigned budget, what would be an optimal strategy to select the most qualified candidate? We model the above problem as a multicolor secretary problem, allowing comparisons be…

This https://arxiv.org/abs/2303.09205 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csGT_…

Addressing bias in online selection with limited budget of comparisons
Consider a hiring process with candidates coming from different universities. It is easy to order candidates with the same background, yet it can be challenging to compare them otherwise. The latter case requires additional costly assessments, leading to a potentially high total cost for the hiring organization. Given an assigned budget, what would be an optimal strategy to select the most qualified candidate? We model the above problem as a multicolor secretary problem, allowing comparisons be…

This https://arxiv.org/abs/2111.03174 has been replaced.
link: https://scholar.google.com/scholar?q=a

Single-Sample Prophet Inequalities via Greedy-Ordered Selection
We study single-sample prophet inequalities (SSPIs), i.e., prophet inequalities where only a single sample from each prior distribution is available. Besides a direct, and optimal, SSPI for the basic single choice problem [Rubinstein et al., 2020], most existing SSPI results were obtained via an elegant, but inherently lossy, reduction to order-oblivious secretary (OOS) policies [Azar et al., 2014]. Motivated by this discrepancy, we develop an intuitive and versatile greedy-based technique that…