| A confidence-based approach for balancing fairness and accuracy B Fish, J Kun, ÁD Lelkes Proceedings of the 2016 SIAM international conference on data mining, 144-152, 2016 | 273 | 2016 |
| Gaps in Information Access in Social Networks B Fish, A Bashardoust, D Boyd, S Friedler, C Scheidegger, ... The World Wide Web Conference, 480-490, 2019 | 65 | 2019 |
| When not to design, build, or deploy S Barocas, AJ Biega, B Fish, J Niklas, L Stark Proceedings of the 2020 Conference on Fairness, Accountability, and …, 2020 | 40 | 2020 |
| Feature selection based on mutual information for human activity recognition B Fish, A Khan, NH Chehade, C Chien, G Pottie 2012 IEEE International Conference on Acoustics, Speech and Signal …, 2012 | 38 | 2012 |
| Fair boosting: a case study B Fish, J Kun, AD Lelkes Workshop on Fairness, Accountability, and Transparency in Machine Learning, 5, 2015 | 34 | 2015 |
| On the computational complexity of mapreduce B Fish, J Kun, AD Lelkes, L Reyzin, G Turán Distributed Computing: 29th International Symposium, DISC 2015, Tokyo, Japan …, 2015 | 32 | 2015 |
| Reflexive design for fairness and other human values in formal models B Fish, L Stark Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society, 89-99, 2021 | 26 | 2021 |
| On the complexity of learning from label proportions B Fish, L Reyzin arXiv preprint arXiv:2004.03515, 2020 | 19 | 2020 |
| A supervised approach to time scale detection in dynamic networks B Fish, RS Caceres arXiv preprint arXiv:1702.07752, 2017 | 14* | 2017 |
| Handling oversampling in dynamic networks using link prediction B Fish, RS Caceres Machine Learning and Knowledge Discovery in Databases: European Conference …, 2015 | 13 | 2015 |
| The effects of competition and regulation on error inequality in data-driven markets H Elzayn, B Fish Proceedings of the 2020 conference on fairness, accountability, and …, 2020 | 10 | 2020 |
| Recovering Social Networks by Observing Votes B Fish, Y Huang, L Reyzin | 10 | 2016 |
| danah boyd, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. Gaps in information access in social networks B Fish, A Bashardoust WWW2019, 480-490, 2019 | 9 | 2019 |
| A task-driven approach to time scale detection in dynamic networks B Fish, RS Caceres Proceedings of the 13th international workshop on mining and learning with …, 2017 | 9 | 2017 |
| Diamond-free subsets in the linear lattices G Sarkis, S Shahriari, PCURC PCURC@ sakai. claremont. edu Order 31, 421-433, 2014 | 9 | 2014 |
| Sampling without compromising accuracy in adaptive data analysis B Fish, L Reyzin, BIP Rubinstein Algorithmic Learning Theory, 297-318, 2020 | 8* | 2020 |
| On graph neural network fairness in the presence of heterophilous neighborhoods D Loveland, J Zhu, M Heimann, B Fish, MT Schaub, D Koutra arXiv preprint arXiv:2207.04376, 2022 | 7 | 2022 |
| Zero-sum flows of the linear lattice G Sarkis, S Shahriari Finite Fields and Their Applications 31, 108-120, 2015 | 7 | 2015 |
| It’s not fairness, and it’s not fair: the failure of distributional equality and the promise of relational equality in complete-information hiring games B Fish, L Stark Proceedings of the 2nd ACM Conference on Equity and Access in Algorithms …, 2022 | 5 | 2022 |
| On performance discrepancies across local homophily levels in graph neural networks D Loveland, J Zhu, M Heimann, B Fish, MT Schaub, D Koutra Learning on Graphs Conference, 6: 1-6: 30, 2024 | 4 | 2024 |
Jeremy KunGoogle, Inc.Verified email at google.com
