Research Papers
Fairness including Bias and Privacy Research
Estimation of Fair Ranking Metrics with Incomplete Judgments. Omer Kirnap, Fernando Diaz, Asia Biega and Emine Yilmaz. WWW 2021: 1065-1075
Evaluation metrics for measuring bias in search engine results. Gizem Gezici, Yucel Saygin, Aldo Lipani and Emine Yilmaz. Information Retrieval Journal 24(2): pp. 85-113, 2021.
Investigating and Mitigating Biases in Crowdsourced Data. Danula Hettiachchi, Mark Sanderson, Jorge Gonçalves, Simo Hosio, Gabriella Kazai, Matthew Lease, Mike Schaekermann, Emine Yilmaz. CSCW Companion 2021: 331-334
Auditing Search Engines for Differential Satisfaction Across Demographics. Rishabh Mehrotra, Ashton Anderson, Fernando Diaz, Amit Sharma, Hanna M. Wallach, Emine Yilmaz. The Web Conference 2017, pp. 626-633.
FACTS-IR: fairness, accountability, confidentiality, transparency, and safety in information retrieval. A. Oltenau et al., SIGIR Forum 53(2): 20-43 (2019).
Learning to Noise: Application-Agnostic Data Sharing with Local Differential Privacy. Alex Mansbridge, Gregory Barbour, Davide Piras, Christopher Frye, Ilya Feige, David Barber. arXiv:2010.12464 (2021).
Private Machine Learning via Randomised Response. David Barber. arXiv:2001.04942 (2020)
Towards Algorithm Auditing: A Survey on Managing Legal, Ethical and Technological Risks of AI, ML and Associated Algorithms. Koshiyama et al, SSRN 2021.
Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting. H Ritter, A Botev, D Barber. Neural Information Processing Systems (2018)
Practical gauss-newton optimisation for deep learning. H Ritter, A Botev, D Barber (2017)
Graphical models for time-series. D Barber, AT Cemgil (2010)
