[60] E. Patrick and F. Fischer. Nonparametric feature selection. IEEE Transactions on Information Theory, 15(5):577-584, 1969.
[61] Y. Polyanskiy. Lecture notes on  $f$ -divergences, 2019. Lecture notes, https://people.lids.mit.edu/yp/homepage/data/LN_fdiv.pdf.
[62] R. Qiao, X. Xu, and B. K. H. Low. Collaborative causal inference with fair incentives. In Proc. ICML, pages 28300-28320, 2023.
[63] T. W. Rauber, T. Braun, and K. Berns. Probabilistic distance measures of the Dirichlet and Beta distributions. Pattern Recognition, 41(2):637-645, 2008.
[64] S. Romano, N. X. Vinh, J. Bailey, and K. Verspoor. Adjusting for chance clustering comparison measures. Journal of Machine Learning Research, 17(1):4635-4666, 2016.
[65] G. Ronning. Maximum likelihood estimation of Dirichlet distributions. Journal of Statistical Computation and Simulation, 32(4):215-221, 1989.
[66] B. Rozemberczki and R. Sarkar. The Shapley value of classifiers in ensemble games. In Proc. CIKM, pages 1558-1567, 2021.
[67] S. Sagawa, P. W. Koh, T. B. Hashimoto, and P. Liang. Distributionally robust neural networks. In Proc. ICLR, 2020.
[68] M. Sensoy, L. Kaplan, and M. Kandemir. Evidential deep learning to quantify classification uncertainty. In Proc. NeurIPS, 2018.
[69] L. S. Shapley. A value for  $n$ -person games. In H. W. Kuhn and A. W. Tucker, editors, Contributions to the Theory of Games, volume 2, pages 307-317. Princeton University Press, 1953.
[70] R. H. L. Sim, X. Xu, and B. K. H. Low. Data valuation in machine learning: "ingredients", strategies, and open challenges. In Proc. IJCAI-22, pages 5607–5614, 2022. Survey Track.
[71] R. H. L. Sim, Y. Zhang, M. C. Chan, and B. K. H. Low. Collaborative machine learning with incentive-aware model rewards. In Proc. ICML, 2020.
[72] R. H. L. Sim, Y. Zhang, T. N. Hoang, X. Xu, B. K. H. Low, and P. Jaillet. Incentives in private collaborative machine learning. In Proc. NeurIPS, 2023.
[73] K. Singhal, H. Sidahmed, Z. Garrett, S. Wu, J. K. Rush, and S. Prakash. Federated reconstruction: Partially local federated learning. In Proc. NeurIPS, 2021.
[74] S. S. Tay, X. Xu, C. S. Foo, and B. K. H. Low. Incentivizing collaboration in machine learning via synthetic data rewards. In Proc. AAAI, volume 36, pages 9448-9456, 2022.
[75] C. Tosh and S. Dasgupta. The relative complexity of maximum likelihood estimation, MAP estimation, and sampling. In Proc. COLT, pages 2993-3035, 2019.
[76] T. Wang, Y. Yang, and R. Jia. Learnability of learning performance and its application to data valuation, 2021.
[77] L. Watson, R. Andreeva, H.-T. Yang, and R. Sarkar. Differentially private Shapley values for data evaluation, 2022.
[78] Z. Wu, Y. Shu, and B. K. H. Low. DAVINZ: Data valuation using deep neural networks at initialization. In Proc. ICML, pages 24150-24176, 2022.
[79] X. Xu, L. Lyu, X. Ma, C. Miao, C. S. Foo, and B. K. H. Low. Gradient driven rewards to guarantee fairness in collaborative machine learning. In Proc. NeurIPS, 2021.
[80] X. Xu, Z. Wu, C.-S. Foo, and B. K. H. Low. Validation free and replication robust volume-based data valuation. In Proc. NeurIPS, 2021.
[81] X. Xu, Z. Wu, A. Verma, C. S. Foo, and B. K. H. Low. Fair: Fair collaborative active learning with individual rationality for scientific discovery. In Proc. AISTATS, pages 4033-4057, 2023.
[82] Z. Zhou, X. Xu, R. H. L. Sim, and C.-S. F. B. K. H. Low. Probably approximate Shapley fairness with applications in machine learning. In Proc. AAAI, 2023.