AQuA: A Benchmarking Tool for Label Quality Assessment

1 code implementation • NeurIPS 2023 • Mononito Goswami, Vedant Sanil, Arjun Choudhry, Arvind Srinivasan, Chalisa Udompanyawit, Artur Dubrawski

We hope that our proposed design space and benchmark enable practitioners to choose the right tools to improve their label quality and that our benchmark enables objective and rigorous evaluation of machine learning tools facing mislabeled data.

Benchmarking Label Error Detection +1

Recovering Sparse and Interpretable Subgroups with Heterogeneous Treatment Effects with Censored Time-to-Event Outcomes

no code implementations • 24 Feb 2023 • Chirag Nagpal, Vedant Sanil, Artur Dubrawski

In this paper we propose a statistical approach to recovering sparse phenogroups (or subtypes) that demonstrate differential treatment effects as compared to the study population.