56 citations · 122 across the 22 of their papers we have counts for
4 papers · 1 filter
Navigating Ensemble Configurations for Algorithmic Fairness
Michael Feffer, Martin Hirzel, Samuel C. Hoffman +3
Bias mitigators can improve algorithmic fairness in machine learning models, but their effect on fairness is often not stable across data splits. A popular approach to train more s…
An Empirical Study of Modular Bias Mitigators and Ensembles
Michael Feffer, Martin Hirzel, Samuel C. Hoffman +3
There are several bias mitigators that can reduce algorithmic bias in machine learning models but, unfortunately, the effect of mitigators on fairness is often not stable when meas…
Lale: Consistent Automated Machine Learning
Guillaume Baudart, Martin Hirzel, Kiran Kate +2
Automated machine learning makes it easier for data scientists to develop pipelines by searching over possible choices for hyperparameters, algorithms, and even pipeline topologies…
Compiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
Guillaume Baudart, Javier Burroni, Martin Hirzel +2
Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from…