51 citations · 69 across the 12 of their papers we have counts for
4 papers · 1 filter
Counterfactual Mean-variance Optimization
Kwangho Kim, Alan Mishler, José R. Zubizarreta
We study a counterfactual mean-variance optimization, where the mean and variance are defined as functionals of counterfactual distributions. The optimization problem defines the o…
Flexible Group Fairness Metrics for Survival Analysis
Raphael Sonabend, Florian Pfisterer, Alan Mishler +4
Algorithmic fairness is an increasingly important field concerned with detecting and mitigating biases in machine learning models. There has been a wealth of literature for algorit…
Fairness via In-Processing in the Over-parameterized Regime: A Cautionary Tale
Akshaj Kumar Veldanda, Ivan Brugere, Jiahao Chen +3
The success of DNNs is driven by the counter-intuitive ability of over-parameterized networks to generalize, even when they perfectly fit the training data. In practice, test error…
Fair When Trained, Unfair When Deployed: Observable Fairness Measures are Unstable in Performative Prediction Settings
Alan Mishler, Niccolò Dalmasso
Many popular algorithmic fairness measures depend on the joint distribution of predictions, outcomes, and a sensitive feature like race or gender. These measures are sensitive to d…