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Modeling Content Creator Incentives on Algorithm-Curated Platforms



Jiri Hron , Karl Krauth , Michael I. Jordan , Niki Kilbertus , Sarah Dean


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Multi-disciplinary fairness considerations in machine learning for clinical trials



Isabel Chien , Nina Deliu , Richard E. Turner , Adrian Weller , Sofia S. Villar , Niki Kilbertus

* Appeared at ACM FAccT 2022 

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Supervised Learning and Model Analysis with Compositional Data



Shimeng Huang , Elisabeth Ailer , Niki Kilbertus , Niklas Pfister


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Predicting single-cell perturbation responses for unseen drugs



Leon Hetzel , Simon Böhm , Niki Kilbertus , Stephan Günnemann , Mohammad Lotfollahi , Fabian Theis

* ICLR 2022 workshop paper at Workshop "Machine Learning for Drug Discovery" 
* 8 pages 

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Stochastic Causal Programming for Bounding Treatment Effects



Kirtan Padh , Jakob Zeitler , David Watson , Matt Kusner , Ricardo Silva , Niki Kilbertus


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On component interactions in two-stage recommender systems



Jiri Hron , Karl Krauth , Michael I. Jordan , Niki Kilbertus


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Beyond Predictions in Neural ODEs: Identification and Interventions



Hananeh Aliee , Fabian J. Theis , Niki Kilbertus


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A causal view on compositional data



Elisabeth Ailer , Christian L. Müller , Niki Kilbertus

* Code available on https://github.com/EAiler/comp-iv 

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Beyond traditional assumptions in fair machine learning



Niki Kilbertus

* PhD Thesis submitted at the University of Cambridge, October 2020. The thesis is based on a number of previous works also available on arxiv (see Chapter 1) 

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Exploration in two-stage recommender systems



Jiri Hron , Karl Krauth , Michael I. Jordan , Niki Kilbertus

* Published at the REVEAL 2020 workshop (RecSys 2020) 

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