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Found 7 papers, 3 papers with code
Policy Learning under Biased Sample Selection
Practitioners often use data from a randomized controlled trial to learn a treatment assignment policy that can be deployed on a target population.
Learning from a Biased Sample
Applying the distributionally robust optimization framework, we propose a method for learning a decision rule that minimizes the worst-case risk incurred under a family of test distributions that can generate the training distribution under $\Gamma$-biased sampling.
Policy Learning with Competing Agents
Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat.
Reliable Decisions with Threshold Calibration
We propose a stronger notion of calibration called threshold calibration, which is exactly the condition required to ensure that decision loss is predicted accurately for threshold decisions.
Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration
In this work, we introduce a new notion -- \emph{decision calibration} -- that requires the predicted distribution and true distribution to be ``indistinguishable'' to a set of downstream decision-makers.
Unsupervised Domain Adaptation in the Absence of Source Data
Current unsupervised domain adaptation methods can address many types of distribution shift, but they assume data from the source domain is freely available.
Deep Orientation Uncertainty Learning based on a Bingham Loss
Reasoning about uncertain orientations is one of the core problems in many perception tasks such as object pose estimation or motion estimation.
