[38] H. Jeffreys. An invariant form for the prior probability in estimation problems. Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 186(1007):453-461, 1946.
[39] R. Jia, D. Dao, B. Wang, F. A. Hubis, N. Hynes, N. M. Gürel, B. Li, C. Zhang, D. Song, and C. J. Spanos. Towards efficient data valuation based on the shapley value. In Proc. AISTATS, pages 1167-1176, 2019.
[40] G. A. Kaissis, M. R. Makowski, D. Rückert, and R. F. Braren. Secure, privacy-preserving and federated machine learning in medical imaging. Nature Machine Intelligence, 2:305–311, 2020.
[41] A. Krizhevsky. Learning multiple layers of features from tiny images. Master's thesis, Department of Computer Science, University of Toronto, 2009.
[42] S. Kullback and R. A. Leibler. On information and sufficiency. The Annals of Mathematical Statistics, 22(1):79 - 86, 1951.
[43] A. Kurakin, I. J. Goodfellow, and S. Bengio. Adversarial machine learning at scale. In Proc. ICLR, 2017.
[44] T. C. Lam, N. Hoang, B. K. H. Low, and P. Jaillet. Model fusion for personalized learning. In Proc. ICML, pages 5948-5958, 2021.
[45] Y. LeCun, B. Boser, J. Denker, D. Henderson, R. Howard, W. Hubbard, and L. Jackel. Handwritten digit recognition with a back-propagation network. In Proc. NeurIPS, 1990.
[46] Y. Lecun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278-2324, 1998.
[47] A. Lederer, J. Umlauft, and S. Hirche. Uniform error bounds for Gaussian process regression with application to safe control. In Proc. NeurIPS, 2019.
[48] J. Lin. On the Dirichlet distribution., 2016. Master's thesis, Department of Mathematics and Statistics, Queen's University.
[49] X. Lin, X. Xu, S.-K. Ng, C.-S. Foo, and B. K. H. Low. Fair yet asymptotically equal collaborative learning. In Proc. ICML, pages 21223-21259, 2023.
[50] T. Lissack and K.-S. Fu. Error estimation in pattern recognition via  $l_{\alpha}$ -distance between posterior density functions. IEEE Transactions on Information Theory, 22(1):34-45, 1976.
[51] J. Liu, J. Lou, J. Liu, L. Xiong, J. Pei, and J. Sun. Dealer: An end-to-end model marketplace with differential privacy. Proc. VLDB Endow., 14(6):957-969, 2021.
[52] B. K. H. Low, J. Yu, J. Chen, and P. Jaillet. Parallel gaussian process regression for big data: Low-rank representation meets markov approximation. In Proc. AAAI, page 2821-2827, 2015.
[53] S. Maleki, L. Tran-Thanh, G. Hines, T. Rahwan, and A. Rogers. Bounding the estimation error of sampling-based Shapley value approximation with/without stratifying, 2013.
[54] A. Malinin. Uncertainty estimation in deep learning with application to spoken language assessment. PhD thesis, Department of Engineering, University of Cambridge, 2019.
[55] A. Malinin and M. Gales. Predictive uncertainty estimation via prior networks. In Proc. NeurIPS, 2018.
[56] N. Mehrabi, F. Morstatter, N. Saxena, K. Lerman, and A. Galstyan. A survey on bias and fairness in machine learning. ACM Computing Surveys, 54(6), 2021.
[57] T. Minka. Estimating a dirichlet distribution, 2000.
[58] MONAI Consortium. MONAI: Medical open network for AI. https://github.com/Project-MONAI/MONAI, 2020. Version 0.6.0.
[59] Q. P. Nguyen, B. K. H. Low, and P. Jaillet. Trade-off between payoff and model rewards in shapley-fair collaborative machine learning. In Proc. NeurIPS, pages 30542-30553, 2022.