219 citations · 1.8k across the 99 of their papers we have counts for
7 papers · 1 filter
An Empirical Study on Learning Fairness Metrics for COMPAS Data with Human Supervision
Hanchen Wang, Nina Grgic-Hlaca, Preethi Lahoti +2
The notion of individual fairness requires that similar people receive similar treatment. However, this is hard to achieve in practice since it is difficult to specify the appropri…
DADI: Dynamic Discovery of Fair Information with Adversarial Reinforcement Learning
Michiel A. Bakker, Duy Patrick Tu, Humberto Riverón Valdés +4
We introduce a framework for dynamic adversarial discovery of information (DADI), motivated by a scenario where information (a feature set) is used by third parties with unknown ob…
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
Julius von Kügelgen, Paul K Rubenstein, Bernhard Schölkopf +1
We study the problem of causal discovery through targeted interventions. Starting from few observational measurements, we follow a Bayesian active learning approach to perform thos…
Explainable Machine Learning in Deployment
Umang Bhatt, Alice Xiang, Shubham Sharma +7
Explainable machine learning offers the potential to provide stakeholders with insights into model behavior by using various methods such as feature importance scores, counterfactu…
The Sensitivity of Counterfactual Fairness to Unmeasured Confounding
Niki Kilbertus, Philip J. Ball, Matt J. Kusner +2
Causal approaches to fairness have seen substantial recent interest, both from the machine learning community and from wider parties interested in ethical prediction algorithms. In…
Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models: Extension
Yunfei Teng, Wenbo Gao, Francois Chalus +3
We consider distributed optimization under communication constraints for training deep learning models. We propose a new algorithm, whose parameter updates rely on two forces: a re…