Slide 27
Slide 27 text
References
● Lu J., Liu A., Dong F., Gu F., Gama J. and Zhang G. Learning under Concept Drift: A
Review, arXiv:2004.05785 (2020). (link)
● Zeniseka, J., Holzingera, F. and Affenzellera, M. Machine learning based concept drift
detection for predictive maintenance, Computers & Industrial Engineering 137 (2019)
106031.
● Gonçalves P. M., Carvalho Santos S.G.T., Barros, R.S.M. and Vieira D.C.L. A comparative
study on concept drift detectors, Expert Systems with Applications 41 (2014) 8144–8156
● Vela, D., Sharp, A., Zhang, R. et al. Temporal quality degradation in AI models. Sci Rep 12,
11654 (2022). (link)
● Shevlane T., Farquhar S., Garfinkel B., Phuong M., Whittlestone J., Leung J, Kokotajlo D.,
Marchal N., Anderljung M., Kolt N., Ho L., Siddarth D., Avin S., Hawkins W., Kim B., Gabriel
I., Bolina V., Clark J., Bengio Y., Christiano P. and Dafoe A. Model evaluation for extreme
risks, arXiv:2305.15324 (link)