TILT adds a target-data penalty on an auxiliary predictor component to induce effective importance weighting for unsupervised domain adaptation under covariate shift.
Title resolution pending
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 4roles
background 1polarities
background 1representative citing papers
A prediction market design that uses online learning to adaptively mix liquidity regimes from cost functions, achieving switching-regret bounds against the best hindsight sequence.
Generalized Wasserstein barycenters on Riemannian manifolds are absolutely continuous when all input measures are absolutely continuous, for strictly convex cost profiles h with singularity at zero, via a geometric approximation approach.
citing papers explorer
-
TILT: Target-induced loss tilting under covariate shift
TILT adds a target-data penalty on an auxiliary predictor component to induce effective importance weighting for unsupervised domain adaptation under covariate shift.
-
Adaptive Liquidity in Prediction Markets via Online Learning
A prediction market design that uses online learning to adaptively mix liquidity regimes from cost functions, achieving switching-regret bounds against the best hindsight sequence.
-
Absolute continuity of generalized Wasserstein barycenters of finitely many measures
Generalized Wasserstein barycenters on Riemannian manifolds are absolutely continuous when all input measures are absolutely continuous, for strictly convex cost profiles h with singularity at zero, via a geometric approximation approach.
- Neural Flow Operators can Approximate any Operator: Abstract Frameworks and Universal Approximations