| Adversarial multi class learning under weak supervision with performance guarantees A Mazzetto, C Cousins, D Sam, SH Bach, E Upfal International Conference on Machine Learning, 7534-7543, 2021 | 39 | 2021 |
| MCRapper: Monte-Carlo Rademacher averages for poset families and approximate pattern mining L Pellegrina, C Cousins, F Vandin, M Riondato ACM Transactions on Knowledge Discovery from Data (TKDD) 16 (6), 1-29, 2022 | 25 | 2022 |
| An axiomatic theory of provably-fair welfare-centric machine learning C Cousins Advances in Neural Information Processing Systems 34, 16610-16621, 2021 | 25 | 2021 |
| Improved algorithms for learning equilibria in simulation-based games E Areyan Viqueira, C Cousins, A Greenwald Proceedings of the 19th International Conference on Autonomous Agents and …, 2020 | 24 | 2020 |
| Towards interactive curation & automatic tuning of ml pipelines C Binnig, B Buratti, Y Chung, C Cousins, T Kraska, Z Shang, E Upfal, ... Proceedings of the Second Workshop on Data Management for End-To-End Machine …, 2018 | 21 | 2018 |
| Bavarian: Betweenness Centrality Approximation with Variance-aware Rademacher Averages C Cousins, C Wohlgemuth, M Riondato ACM Transactions on Knowledge Discovery from Data 17 (6), 1-47, 2023 | 20 | 2023 |
| CaDET: interpretable parametric conditional density estimation with decision trees and forests C Cousins, M Riondato Machine Learning 108, 1613-1634, 2019 | 19 | 2019 |
| Empirical mechanism design: Designing mechanisms from data EA Viqueira, C Cousins, Y Mohammad, A Greenwald Uncertainty in Artificial Intelligence, 1094-1104, 2020 | 18 | 2020 |
| Sharp uniform convergence bounds through empirical centralization C Cousins, M Riondato Advances in Neural Information Processing Systems 33, 15123-15132, 2020 | 16 | 2020 |
| Learning simulation-based games from data EA Viqueira, C Cousins Proceeding AAMAS'19 Proceedings of the 18th International Conference on …, 2019 | 15 | 2019 |
| Uncertainty and the social planner’s problem: Why sample complexity matters C Cousins Proceedings of the 2022 ACM Conference on Fairness, Accountability, and …, 2022 | 10 | 2022 |
| Learning equilibria of simulation-based games EA Viqueira, C Cousins, E Upfal, A Greenwald arXiv preprint arXiv:1905.13379, 2019 | 10 | 2019 |
| Revisiting fair-PAC learning and the axioms of cardinal welfare C Cousins International Conference on Artificial Intelligence and Statistics, 6422-6442, 2023 | 8 | 2023 |
| Fair E3: Efficient welfare-centric fair reinforcement learning C Cousins, K Asadi, ML Littman 5th Multidisciplinary Conference on Reinforcement Learning and Decision …, 2022 | 7 | 2022 |
| Making mean-estimation more efficient using an MCMC trace variance approach: DynaMITE C Cousins, S Haddadan, E Upfal arXiv preprint arXiv:2011.11129, 2020 | 7 | 2020 |
| Fast doubly-adaptive MCMC to estimate the gibbs partition function with weak mixing time bounds S Haddadan, Y Zhuang, C Cousins, E Upfal Advances in Neural Information Processing Systems 34, 25760-25772, 2021 | 6 | 2021 |
| Towards interactive data exploration C Binnig, F Basık, B Buratti, U Cetintemel, Y Chung, A Crotty, C Cousins, ... Real-Time Business Intelligence and Analytics: International Workshops …, 2019 | 6 | 2019 |
| Decentering imputation: fair learning at the margins of demographics E Dong, C Cousins Queer in AI Workshop@ ICML, 2022 | 5 | 2022 |
| Bounds and Applications of Concentration of Measure in Fair Machine Learning and Data Science. C Cousins Brown University, USA, 2021 | 5 | 2021 |
| The good, the bad and the submodular: Fairly allocating mixed manna under order-neutral submodular preferences C Cousins, V Viswanathan, Y Zick International Conference on Web and Internet Economics, 207-224, 2023 | 4 | 2023 |
Eli UpfalComputer Science Professor, Brown UniversityVerified email at brown.edu
