26 citations · 56 across the 21 of their papers we have counts for
6 papers · 1 filter
Adaptive Estimation and Inference in Semi-parametric Heterogeneous Clustered Multitask Learning via Neyman Orthogonality
Hanxiao Chen, Debarghya Mukherjee
We study clustered multitask learning in a semiparametric setting where tasks share a latent cluster structure in their target parameters but exhibit heterogeneous, potentially inf…
CINDES: Classification induced neural density estimator and simulator
Dehao Dai, Jianqing Fan, Yihong Gu +1
Neural network-based methods for (un)conditional density estimation have recently gained substantial attention, as various neural density estimators have outperformed classical app…
Domain Adaptation meets Individual Fairness. And they get along
Debarghya Mukherjee, Felix Petersen, Mikhail Yurochkin +1
Many instances of algorithmic bias are caused by distributional shifts. For example, machine learning (ML) models often perform worse on demographic groups that are underrepresente…
Post-processing for Individual Fairness
Felix Petersen, Debarghya Mukherjee, Yuekai Sun +1
Post-processing in algorithmic fairness is a versatile approach for correcting bias in ML systems that are already used in production. The main appeal of post-processing is that it…
Does enforcing fairness mitigate biases caused by subpopulation shift?
Subha Maity, Debarghya Mukherjee, Mikhail Yurochkin +1
Many instances of algorithmic bias are caused by subpopulation shifts. For example, ML models often perform worse on demographic groups that are underrepresented in the training da…
Two Simple Ways to Learn Individual Fairness Metrics from Data
Debarghya Mukherjee, Mikhail Yurochkin, Moulinath Banerjee +1
Individual fairness is an intuitive definition of algorithmic fairness that addresses some of the drawbacks of group fairness. Despite its benefits, it depends on a task specific f…