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Capturing Perspectives of Crowdsourced Annotators in Subjective Learning Tasks
Previous studies have shed light on the pitfalls of label aggregation and have introduced a handful of practical approaches to tackle this issue.
Ensembled Prediction Intervals for Causal Outcomes Under Hidden Confounding
We present a simple approach to partial identification using existing causal sensitivity models and show empirically that Caus-Modens gives tighter outcome intervals, as measured by the necessary interval size to achieve sufficient coverage.
Partial Identification of Dose Responses with Hidden Confounders
Sensitivity analyses provide principled ways to give bounds on causal estimates when confounding variables are hidden.
A Metric Space for Point Process Excitations
A multivariate Hawkes process enables self- and cross-excitations through a triggering matrix that behaves like an asymmetrical covariance structure, characterizing pairwise interactions between the event types.
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