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2020 – today
- 2024
[c5]Alexandru Tifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami, Flavien Prost:
FRAPPÉ: A Group Fairness Framework for Post-Processing Everything. ICML 2024- [i10]James Atwood, Preethi Lahoti, Ananth Balashankar, Flavien Prost, Ahmad Beirami:
Inducing Group Fairness in LLM-Based Decisions. CoRR abs/2406.16738 (2024) - 2023
- [i9]James Atwood, Tina Tian, Ben Packer, Meghana Deodhar, Jilin Chen, Alex Beutel, Flavien Prost, Ahmad Beirami:
Towards A Scalable Solution for Improving Multi-Group Fairness in Compositional Classification. CoRR abs/2307.05728 (2023) - [i8]Alexandru Tifrea, Preethi Lahoti, Ben Packer, Yoni Halpern, Ahmad Beirami, Flavien Prost:
FRAPPÉ: A Post-Processing Framework for Group Fairness Regularization. CoRR abs/2312.02592 (2023) - 2022
- [i7]Flavien Prost, Ben Packer, Jilin Chen, Li Wei, Pierre Kremp, Nick Blumm, Susan Wang, Tulsee Doshi, Tonia Osadebe, Lukasz Heldt, Ed H. Chi, Alex Beutel:
Simpson's Paradox in Recommender Fairness: Reconciling differences between per-user and aggregated evaluations. CoRR abs/2210.07755 (2022) - 2021
- [c4]Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. AIES 2021: 873-883 - [c3]Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi:
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. KDD 2021: 1748-1757 - [c2]Xuezhi Wang, Nithum Thain, Anu Sinha, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel:
Practical Compositional Fairness: Understanding Fairness in Multi-Component Recommender Systems. WSDM 2021: 436-444 - [i6]Sirui Yao, Yoni Halpern, Nithum Thain, Xuezhi Wang, Kang Lee, Flavien Prost, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Recommender System Effects with Simulated Users. CoRR abs/2101.04526 (2021) - [i5]Flavien Prost, Pranjal Awasthi, Nick Blumm, Aditee Kumthekar, Trevor Potter, Li Wei, Xuezhi Wang, Ed H. Chi, Jilin Chen, Alex Beutel:
Measuring Model Fairness under Noisy Covariates: A Theoretical Perspective. CoRR abs/2105.09985 (2021) - [i4]Yuyan Wang, Xuezhi Wang, Alex Beutel, Flavien Prost, Jilin Chen, Ed H. Chi:
Understanding and Improving Fairness-Accuracy Trade-offs in Multi-Task Learning. CoRR abs/2106.02705 (2021) - 2020
- [c1]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. NeurIPS 2020 - [i3]Preethi Lahoti, Alex Beutel, Jilin Chen, Kang Lee, Flavien Prost, Nithum Thain, Xuezhi Wang, Ed H. Chi:
Fairness without Demographics through Adversarially Reweighted Learning. CoRR abs/2006.13114 (2020)
2010 – 2019
- 2019
- [i2]Flavien Prost, Nithum Thain, Tolga Bolukbasi:
Debiasing Embeddings for Reduced Gender Bias in Text Classification. CoRR abs/1908.02810 (2019) - [i1]Flavien Prost, Hai Qian, Qiuwen Chen, Ed H. Chi, Jilin Chen, Alex Beutel:
Toward a better trade-off between performance and fairness with kernel-based distribution matching. CoRR abs/1910.11779 (2019)
Coauthor Index
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