147 citations · 696 across the 57 of their papers we have counts for
6 papers · 1 filter
On sensitivity of meta-learning to support data
Mayank Agarwal, Mikhail Yurochkin, Yuekai Sun
Meta-learning algorithms are widely used for few-shot learning. For example, image recognition systems that readily adapt to unseen classes after seeing only a few labeled examples…
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…
k-Mixup Regularization for Deep Learning via Optimal Transport
Kristjan Greenewald, Anming Gu, Mikhail Yurochkin +2
Mixup is a popular regularization technique for training deep neural networks that improves generalization and increases robustness to certain distribution shifts. It perturbs inpu…
Individually Fair Gradient Boosting
Alexander Vargo, Fan Zhang, Mikhail Yurochkin +1
We consider the task of enforcing individual fairness in gradient boosting. Gradient boosting is a popular method for machine learning from tabular data, which arise often in appli…
Statistical inference for individual fairness
Subha Maity, Songkai Xue, Mikhail Yurochkin +1
As we rely on machine learning (ML) models to make more consequential decisions, the issue of ML models perpetuating or even exacerbating undesirable historical biases (e.g., gende…
Individually Fair Ranking
Amanda Bower, Hamid Eftekhari, Mikhail Yurochkin +1
We develop an algorithm to train individually fair learning-to-rank (LTR) models. The proposed approach ensures items from minority groups appear alongside similar items from major…