183 citations · 195 across the 5 of their papers we have counts for
6 papers
Beyond traditional assumptions in fair machine learning
Niki Kilbertus
This thesis scrutinizes common assumptions underlying traditional machine learning approaches to fairness in consequential decision making. After challenging the validity of these…
Exploration in two-stage recommender systems
Jiri Hron, Karl Krauth, Michael I. Jordan +1
Two-stage recommender systems are widely adopted in industry due to their scalability and maintainability. These systems produce recommendations in two steps: (i) multiple nominato…
Convolutional neural networks: a magic bullet for gravitational-wave detection?
Timothy D. Gebhard, Niki Kilbertus, Ian Harry +1
In the last few years, machine learning techniques, in particular convolutional neural networks, have been investigated as a method to replace or complement traditional matched fil…
Fair Decisions Despite Imperfect Predictions
Niki Kilbertus, Manuel Gomez-Rodriguez, Bernhard Schölkopf +2
Consequential decisions are increasingly informed by sophisticated data-driven predictive models. However, to consistently learn accurate predictive models, one needs access to gro…
Generalization in anti-causal learning
Niki Kilbertus, Giambattista Parascandolo, Bernhard Schölkopf
The ability to learn and act in novel situations is still a prerogative of animate intelligence, as current machine learning methods mostly fail when moving beyond the standard i.i…
Avoiding Discrimination through Causal Reasoning
Niki Kilbertus, Mateo Rojas-Carulla, Giambattista Parascandolo +3
Recent work on fairness in machine learning has focused on various statistical discrimination criteria and how they trade off. Most of these criteria are observational: They depend…