no code implementations • 15 Jun 2022 • Maxime Cauchois, John Duchi
The cost and scarcity of fully supervised labels in statistical machine learning encourage using partially labeled data for model validation as a cheaper and more accessible alternative.
no code implementations • 8 Feb 2022 • Alnur Ali, Maxime Cauchois, John C. Duchi
The statistical machine learning community has demonstrated considerable resourcefulness over the years in developing highly expressive tools for estimation, prediction, and inference.
no code implementations • 20 Jan 2022 • Maxime Cauchois, Suyash Gupta, Alnur Ali, John Duchi
The expense of acquiring labels in large-scale statistical machine learning makes partially and weakly-labeled data attractive, though it is not always apparent how to leverage such data for model fitting or validation.
no code implementations • 10 Aug 2020 • Maxime Cauchois, Suyash Gupta, Alnur Ali, John C. Duchi
One strategy -- coming from robust statistics and optimization -- is thus to build a model robust to distributional perturbations.
no code implementations • 21 Apr 2020 • Maxime Cauchois, Suyash Gupta, John Duchi
We develop conformal prediction methods for constructing valid predictive confidence sets in multiclass and multilabel problems without assumptions on the data generating distribution.