David Madras
David Madras
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Zitiert von
Zitiert von
Learning adversarially fair and transferable representations
D Madras, E Creager, T Pitassi, R Zemel
International Conference on Machine Learning, 3384-3393, 2018
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
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
Predict responsibly: Increasing fairness by learning to defer
D Madras, T Pitassi, R Zemel
Fairness through causal awareness: Learning latent-variable models for biased data
D Madras, E Creager, T Pitassi, R Zemel
arXiv preprint arXiv:1809.02519, 2018
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
Causal modeling for fairness in dynamical systems
E Creager, D Madras, T Pitassi, R Zemel
International conference on machine learning, 2185-2195, 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
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
Detecting extrapolation with local ensembles
D Madras, J Atwood, A D'Amour
International Conference on Learning Representations, 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
Identifying and benchmarking natural out-of-context prediction problems
D Madras, R Zemel
Advances in Neural Information Processing Systems 34, 15344-15358, 2021
Change-point detection methods for body-worn video
S Allen, D Madras, Y Ye, G Zanotti
arXiv preprint arXiv:1610.06453, 2016
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
On meaningful human control in high-stakes machine-human partnerships
L McCoy, J Burkell, D Card, B Davis, J Gichoya, S LePage, D Madras
Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context
M Reid, N Savinov, D Teplyashin, D Lepikhin, T Lillicrap, J Alayrac, ...
arXiv preprint arXiv:2403.05530, 2024
Understanding Post-hoc Adaptation for Improving Subgroup Robustness
D Madras, R Zemel
NeurIPS 2021 Workshop on Distribution Shifts: Connecting Methods and …, 2021
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
Out of the ordinary: Spectrally adapting regression for covariate shift
B Eyre, E Creager, D Madras, V Papyan, R Zemel
arXiv preprint arXiv:2312.17463, 2023
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
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