| Multicalibration: Calibration for the (computationally-identifiable) masses U Hébert-Johnson, M Kim, O Reingold, G Rothblum International Conference on Machine Learning, 1939-1948, 2018 | 473 | 2018 |
| Multiaccuracy: Black-box post-processing for fairness in classification MP Kim, A Ghorbani, J Zou Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 247-254, 2019 | 372 | 2019 |
| Fairness through computationally-bounded awareness M Kim, O Reingold, G Rothblum Advances in neural information processing systems 31, 2018 | 179 | 2018 |
| A distributional framework for data valuation A Ghorbani, M Kim, J Zou International Conference on Machine Learning, 3535-3544, 2020 | 130 | 2020 |
| Planting undetectable backdoors in machine learning models S Goldwasser, MP Kim, V Vaikuntanathan, O Zamir 2022 IEEE 63rd Annual Symposium on Foundations of Computer Science (FOCS …, 2022 | 74 | 2022 |
| Outcome indistinguishability C Dwork, MP Kim, O Reingold, GN Rothblum, G Yona Proceedings of the 53rd Annual ACM SIGACT Symposium on Theory of Computing …, 2021 | 66 | 2021 |
| Calibrating predictions to decisions: A novel approach to multi-class calibration S Zhao, M Kim, R Sahoo, T Ma, S Ermon Advances in Neural Information Processing Systems 34, 22313-22324, 2021 | 56 | 2021 |
| Who can win a single-elimination tournament? MP Kim, W Suksompong, VV Williams SIAM Journal on Discrete Mathematics 31 (3), 1751-1764, 2017 | 50 | 2017 |
| Preference-informed fairness MP Kim, A Korolova, GN Rothblum, G Yona arXiv preprint arXiv:1904.01793, 2019 | 49 | 2019 |
| Universal adaptability: Target-independent inference that competes with propensity scoring MP Kim, C Kern, S Goldwasser, F Kreuter, O Reingold Proceedings of the National Academy of Sciences 119 (4), e2108097119, 2022 | 40 | 2022 |
| Fixing tournaments for kings, chokers, and more MP Kim, VV Williams Proceedings of the 24th International Conference on Artificial Intelligence …, 2015 | 38 | 2015 |
| Synthesis of enantiopure, trisubstituted cryptophane-A derivatives O Taratula, MP Kim, Y Bai, JP Philbin, BA Riggle, DN Haase, ... Organic letters 14 (14), 3580-3583, 2012 | 38 | 2012 |
| Low-degree multicalibration P Gopalan, MP Kim, MA Singhal, S Zhao Conference on Learning Theory, 3193-3234, 2022 | 37 | 2022 |
| Learning from outcomes: Evidence-based rankings C Dwork, MP Kim, O Reingold, GN Rothblum, G Yona 2019 IEEE 60th Annual Symposium on Foundations of Computer Science (FOCS …, 2019 | 29 | 2019 |
| Loss minimization through the lens of outcome indistinguishability P Gopalan, L Hu, MP Kim, O Reingold, U Wieder arXiv preprint arXiv:2210.08649, 2022 | 28 | 2022 |
| On estimating edit distance: Alignment, dimension reduction, and embeddings M Charikar, O Geri, MP Kim, W Kuszmaul arXiv preprint arXiv:1804.09907, 2018 | 18 | 2018 |
| Swap Agnostic Learning, or Characterizing Omniprediction via Multicalibration P Gopalan, MP Kim, O Reingold arXiv preprint arXiv:2302.06726v2, 2023 | 17 | 2023 |
| Making decisions under outcome performativity MP Kim, JC Perdomo 14th Innovations in Theoretical Computer Science Conference (ITCS 2023), 2023 | 16 | 2023 |
| Tracking and improving information in the service of fairness S Garg, MP Kim, O Reingold Proceedings of the 2019 ACM Conference on Economics and Computation, 809-824, 2019 | 12 | 2019 |
| Beyond bernoulli: Generating random outcomes that cannot be distinguished from nature C Dwork, MP Kim, O Reingold, GN Rothblum, G Yona International Conference on Algorithmic Learning Theory, 342-380, 2022 | 10 | 2022 |
