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Online Learning with an Unknown Fairness Metric
Stephen Gillen, Christopher Jung, Michael Kearns, Aaron Roth
We therefore assume that the algorithm has access to an oracle that knows intuitively what it means to be fair, but cannot explicitly enunciate the fairness metric.
Country:
- North America > United States > Massachusetts > Middlesex County > Cambridge (0.04)
- North America > United States > California > Alameda County > Berkeley (0.04)
- North America > Canada > Quebec > Montreal (0.04)
- Europe > Spain > Andalusia > Granada Province > Granada (0.04)
dececdcbf0ea0162234a8fb4ab051415-Supplemental-Conference.pdf
Thus,γ(ω) (0,1] for ω (0,1], which meets the algorithm design requirement. Algorithm 2 actually performs the gradient descent scheme on the function ˆfti(x) = Eu B[fti(x+ϵu)] restricted to the convex set(1 ζ)K.
ReducingtheCovariateShiftbyMirrorSamplesin CrossDomainAlignment
The proposed method does not distort the internal structure of the underlyingdistribution.