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Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations.

F. Prost, B. Packer, J. Chen, L. Wei, P. Kremp, N. Blumm, S. Wang, T. Doshi, T. Osadebe, L. Heldt, E. Chi, and A. Beutel. CoRR, (2022)

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Flavien Picon

Flavien Bouillot

Gerhard Prost

Nicole Prost

Flavien Flandrin

Other publications of authors with the same name

Fairness without Demographics through Adversarially Reweighted Learning.P. Lahoti, A. Beutel, J. Chen, K. Lee, F. Prost, N. Thain, X. Wang, and E. Chi. NeurIPS, (2020)Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification.J. Atwood, T. Tian, B. Packer, M. Deodhar, J. Chen, A. Beutel, F. Prost, and A. Beirami. CoRR, (2023)Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning.Y. Wang, X. Wang, A. Beutel, F. Prost, J. Chen, and E. Chi. KDD, page 1748-1757. ACM, (2021)Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective.F. Prost, P. Awasthi, N. Blumm, A. Kumthekar, T. Potter, L. Wei, X. Wang, E. Chi, J. Chen, and A. Beutel. AIES, page 873-883. ACM, (2021)Measuring Recommender System Effects with Simulated Users.S. Yao, Y. Halpern, N. Thain, X. Wang, K. Lee, F. Prost, E. Chi, J. Chen, and A. Beutel. CoRR, (2021)Toward a better trade-off between performance and fairness with kernel-based distribution matching.F. Prost, H. Qian, Q. Chen, E. Chi, J. Chen, and A. Beutel. CoRR, (2019)Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations.F. Prost, B. Packer, J. Chen, L. Wei, P. Kremp, N. Blumm, S. Wang, T. Doshi, T. Osadebe, L. Heldt and 2 other author(s). CoRR, (2022)FRAPPÉ: A Post-Processing Framework for Group Fairness Regularization.A. Tifrea, P. Lahoti, B. Packer, Y. Halpern, A. Beirami, and F. Prost. CoRR, (2023)Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems.X. Wang, N. Thain, A. Sinha, F. Prost, E. Chi, J. Chen, and A. Beutel. WSDM, page 436-444. ACM, (2021)Debiasing Embeddings for Reduced Gender Bias in Text Classification.F. Prost, N. Thain, and T. Bolukbasi. CoRR, (2019)

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Disambiguation of "Prost, Flavien"

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Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations.

Please choose a person to relate this publication to

Flavien Picon

Flavien Bouillot

Gerhard Prost

Nicole Prost

Flavien Flandrin

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "Prost, Flavien"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations.

Please choose a person to relate this publication to

Flavien Picon

Flavien Bouillot

Gerhard Prost

Nicole Prost

Flavien Flandrin

Other publications of authors with the same name

Disambiguation

copy delete add this publication to your clipboard
community post
history of this post
URL
DOI
BibTeX
EndNote
APA
Chicago
DIN 1505
Harvard
MSOffice XML

Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations.