Isitpossibleto acquire therequired domain expertise, without involving human experts?

Quantitative Relative Judgment Aggregation (QRJA) is a new research topic in (computational) social choice. In the QRJA model, agents provide judgments on the relative quality of different candidates, and the goal is to aggregate these judgments across allagents.

Let (X, Y) XY = N(0, XY) bejointly variables. Thisrateisinline(3), which boundmeaningfulk-SMIisitself k-SMIdecompositionGiven in Gaussian 36,37], we k-SMIintoa(X, Y) µXY 2 P(Rdx Rdy), let(X ,Y ) XY :=N(0, XY)bejointly (X, Y).

The central question is,therefore, tounderstand which noise distribution optimizes the privacy-accuracy trade-off, especially when the dimension of the answer vector ishigh.

Inevitably these systems need to deal with the plethora of practical issues that arise from automation. One important aspect is being able to deal with corrupted or irregular data, either due to poor data collection, the presence of outliers, or adversarial attacks by malicious agents.