Prices generated by automated price experimentation algorithms often display wild fluctuations, leading to unfavorable customer perceptions and violations of individual fairness: e. g., the price seen by a customer can be significantly higher than what was seen by her predecessors, only to fall once again later.
In this paper, we extend the notion of IF to account for the time at which a decision is made, in settings where there exists a notion of conduciveness of decisions as perceived by the affected individuals.
An online labor platform faces an online learning problem in matching workers with jobs and using the performance on these jobs to create better future matches.
We describe an approximate dynamic programming (ADP) approach to compute approximations of the optimal strategies and of the minimal losses that can be guaranteed in discounted repeated games with vector-valued losses.
A major challenge in obtaining large-scale evaluations, e. g., product or service reviews on online platforms, labeling images, grading in online courses, etc., is that of eliciting honest responses from agents in the absence of verifiability.
For every product category, each type has an associated relevance feedback that is assumed to be binary: the category is either relevant or irrelevant.