| Gemini: a family of highly capable multimodal models G Team, R Anil, S Borgeaud, JB Alayrac, J Yu, R Soricut, J Schalkwyk, ... arXiv preprint arXiv:2312.11805, 2023 | 2144 | 2023 |
| Learning adversarially fair and transferable representations D Madras, E Creager, T Pitassi, R Zemel International Conference on Machine Learning, 3384-3393, 2018 | 778 | 2018 |
| Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context G Team, P Georgiev, VI Lei, R Burnell, L Bai, A Gulati, G Tanzer, ... arXiv preprint arXiv:2403.05530, 2024 | 671* | 2024 |
| Flexibly fair representation learning by disentanglement E Creager, D Madras, JH Jacobsen, M Weis, K Swersky, T Pitassi, ... International conference on machine learning, 1436-1445, 2019 | 407 | 2019 |
| Predict responsibly: Increasing fairness by learning to defer D Madras, T Pitassi, R Zemel | 265* | 2018 |
| Fairness through causal awareness: Learning causal latent-variable models for biased data D Madras, E Creager, T Pitassi, R Zemel Proceedings of the conference on fairness, accountability, and transparency …, 2019 | 174 | 2019 |
| Amortized causal discovery: Learning to infer causal graphs from time-series data S Löwe, D Madras, R Zemel, M Welling Conference on Causal Learning and Reasoning, 509-525, 2022 | 163 | 2022 |
| Causal modeling for fairness in dynamical systems E Creager, D Madras, T Pitassi, R Zemel International conference on machine learning, 2185-2195, 2020 | 80 | 2020 |
| Fairness and robustness in invariant learning: A case study in toxicity classification R Adragna, E Creager, D Madras, R Zemel arXiv preprint arXiv:2011.06485, 2020 | 52 | 2020 |
| Participatory approaches to machine learning B Kulynych, D Madras, S Milli, ID Raji, A Zhou, R Zemel International Conference on Machine Learning Workshop 7, 2020 | 43 | 2020 |
| Detecting extrapolation with local ensembles D Madras, J Atwood, A D'Amour International Conference on Learning Representations, 2019 | 31* | 2019 |
| Judging facts, judging norms: Training machine learning models to judge humans requires a modified approach to labeling data A Balagopalan, D Madras, DH Yang, D Hadfield-Menell, GK Hadfield, ... Science Advances 9 (19), eabq0701, 2023 | 22 | 2023 |
| Learning and forgetting unsafe examples in large language models J Zhao, Z Deng, D Madras, J Zou, M Ren arXiv preprint arXiv:2312.12736, 2023 | 21 | 2023 |
| Change-point detection methods for body-worn video S Allen, D Madras, Y Ye, G Zanotti arXiv preprint arXiv:1610.06453, 2016 | 7 | 2016 |
| Identifying and benchmarking natural out-of-context prediction problems D Madras, R Zemel Advances in Neural Information Processing Systems 34, 15344-15358, 2021 | 6 | 2021 |
| Understanding subgroup performance differences of fair predictors using causal models SR Pfohl, N Harris, C Nagpal, D Madras, V Mhasawade, OE Salaudeen, ... NeurIPS 2023 Workshop on Distribution Shifts: New Frontiers with Foundation …, 2023 | 5 | 2023 |
| On meaningful human control in high-stakes machine-human partnerships L McCoy, J Burkell, D Card, B Davis, J Gichoya, S LePage, D Madras | 4 | 2019 |
| Improving fairness in match play golf through enhanced handicap allocation TCY Chan, D Madras, ML Puterman Journal of Sports Analytics 4 (4), 251-262, 2018 | 3 | 2018 |
| Towards Trustworthy Machine Learning in High-Stakes Decision-Making Systems D Madras University of Toronto (Canada), 2023 | 2 | 2023 |
| Understanding Post-hoc Adaptation for Improving Subgroup Robustness D Madras, R Zemel NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021 | 2 | 2021 |
Richard ZemelProfessor of Computer Science, University of TorontoVerified email at cs.toronto.edu
