| 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 | 320 | 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 | 84 | 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 | 47 | 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 | 41 | 2012 |
| 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 | 40 | 2021 |
| Fair boosting: a case study B Fish, J Kun, AD Lelkes Workshop on Fairness, Accountability, and Transparency in Machine Learning, 5, 2015 | 36 | 2015 |
| On the computational complexity of mapreduce B Fish, J Kun, AD Lelkes, L Reyzin, G Turán International symposium on distributed computing, 1-15, 2015 | 34 | 2015 |
| On the complexity of learning from label proportions B Fish, L Reyzin arXiv preprint arXiv:2004.03515, 2020 | 21 | 2020 |
| 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 | 20 | 2024 |
| A supervised approach to time scale detection in dynamic networks B Fish, RS Caceres arXiv preprint arXiv:1702.07752, 2017 | 19* | 2017 |
| 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 | 14 | 2020 |
| Handling oversampling in dynamic networks using link prediction B Fish, RS Caceres Joint European Conference on Machine Learning and Knowledge Discovery in …, 2015 | 13 | 2015 |
| 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 | 12 | 2022 |
| Recovering Social Networks by Observing Votes B Fish, Y Huang, L Reyzin | 12 | 2016 |
| Sampling without compromising accuracy in adaptive data analysis B Fish, L Reyzin, BIP Rubinstein Algorithmic Learning Theory, 297-318, 2020 | 11* | 2020 |
| 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 (3), 421-433, 2014 | 9 | 2014 |
| Zero-sum flows of the linear lattice G Sarkis, S Shahriari Finite Fields and Their Applications 31, 108-120, 2015 | 8 | 2015 |
| Danah boyd, Sorelle A. Friedler, Carlos Scheidegger, and Suresh Venkatasubramanian. 2019. Gaps in information access in social networks B Fish, A Bashardoust The World Wide Web Conference. Association for Computing Machinery, New York …, 0 | 8 | |
| 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 | 7 | 2022 |
Adam D. LelkesGoogle DeepMindVerified email at google.com
