2 citations · 2 across the 1 of their papers we have counts for
4 papers
Achieving Representative Data via Convex Hull Feasibility Sampling Algorithms
Laura Niss, Yuekai Sun, Ambuj Tewari
Sampling biases in training data are a major source of algorithmic biases in machine learning systems. Although there are many methods that attempt to mitigate such algorithmic bia…
What You See May Not Be What You Get: UCB Bandit Algorithms Robust to ε-Contamination
Laura Niss, Ambuj Tewari
Motivated by applications of bandit algorithms in education, we consider a stochastic multi-armed bandit problem with -contaminated rewards. We allow an adversary to g…
Debiasing representations by removing unwanted variation due to protected attributes
Amanda Bower, Laura Niss, Yuekai Sun +1
We propose a regression-based approach to removing implicit biases in representations. On tasks where the protected attribute is observed, the method is statistically more efficien…
Fair Pipelines
Amanda Bower, Sarah N. Kitchen, Laura Niss +3
This work facilitates ensuring fairness of machine learning in the real world by decoupling fairness considerations in compound decisions. In particular, this work studies how fair…