Mining for the Meanings of a Murder: The Impact of OCR Quality on the Use of Digitized Historical Newspapers C Strange, D McNamara, J Wodak, I Wood Digital Humanities Quarterly 8 (1), 2014 | 63 | 2014 |
Costs and benefits of fair representation learning D McNamara, CS Ong, RC Williamson Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 263-270, 2019 | 62 | 2019 |
Provably fair representations D McNamara, CS Ong, RC Williamson arXiv preprint arXiv:1710.04394, 2017 | 55 | 2017 |
Risk bounds for transferring representations with and without fine-tuning D McNamara, MF Balcan International conference on machine learning, 2373-2381, 2017 | 41 | 2017 |
Predicting high impact academic papers using citation network features D McNamara, P Wong, P Christen, KS Ng Pacific-Asia Conference on Knowledge Discovery and Data Mining, 14-25, 2013 | 23 | 2013 |
Trade-offs in Algorithmic Risk Assessment: an Australian Domestic Violence Case Study D McNamara, T Graham, E Broad, CS Ong Theory on Demand #29: Good Data, 96-116, 2019 | 13 | 2019 |
Equalized Odds Implies Partially Equalized Outcomes Under Realistic Assumptions D McNamara AAAI Conference on Artificial Intelligence, Ethics and Society, 2019 | 7 | 2019 |
A modular theory of feature learning D McNamara, CS Ong, RC Williamson arXiv preprint arXiv:1611.03125, 2016 | 4 | 2016 |
Algorithmic Stereotypes: Implications for Fairness of Generalizing from Past Data D McNamara Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society, 535-536, 2019 | | 2019 |
Learning Provably Useful Representations, with Applications to Fairness D McNamara The Australian National University, 2019 | | 2019 |
DHQ: Digital Humanities Quarterly D McNamara DHQ 8 (1), 2014 | | 2014 |