A tropical zeta function for convex domains is defined, shown to have a simple pole at s=2/3 with residue proportional to equiaffine perimeter, and connected to Witten SU(3) zeta for a limit-shape domain.
Learning under selective labels in the presence of expert consistency
2 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We explore the problem of learning under selective labels in the context of algorithm-assisted decision making. Selective labels is a pervasive selection bias problem that arises when historical decision making blinds us to the true outcome for certain instances. Examples of this are common in many applications, ranging from predicting recidivism using pre-trial release data to diagnosing patients. In this paper we discuss why selective labels often cannot be effectively tackled by standard methods for adjusting for sample selection bias, even if there are no unobservables. We propose a data augmentation approach that can be used to either leverage expert consistency to mitigate the partial blindness that results from selective labels, or to empirically validate whether learning under such framework may lead to unreliable models prone to systemic discrimination.
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Introduces a taxonomy of model, feedback, and prediction uncertainty in sequential decisions and demonstrates that accounting for uneven uncertainty across groups can reduce outcome variance for disadvantaged populations while preserving institutional objectives.
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Residues of a tropical zeta function for convex domains
A tropical zeta function for convex domains is defined, shown to have a simple pole at s=2/3 with residue proportional to equiaffine perimeter, and connected to Witten SU(3) zeta for a limit-shape domain.
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Fairness under uncertainty in sequential decisions
Introduces a taxonomy of model, feedback, and prediction uncertainty in sequential decisions and demonstrates that accounting for uneven uncertainty across groups can reduce outcome variance for disadvantaged populations while preserving institutional objectives.