[2] PubChem. https://pubchem.ncbi.nlm.nih.gov/. Accessed: 2022-05-10.
[3] Data Coordinating Center Burton Robert 67 Jensen Mark A 53 Kahn Ari 53 Pihl Todd 53 Pot David 53 Wan Yunhu 53 and Tissue Source Site Levine Douglas A 68. The cancer genome atlas pan-cancer analysis project. Nature genetics, 45(10):1113-1120, 2013.
[4] Anish Agarwal, Munther Dahleh, and Tuhin Sarkar. A marketplace for data: An algorithmic solution. In Proceedings of the 2019 ACM Conference on Economics and Computation, pages 701-726, 2019.
[5] Anish Agarwal, Munther Dahleh, Thibaut Horel, and Maryann Rui. Towards data auctions with externalities. arXiv preprint arXiv:2003.08345, 2020.
[6] Kareem Amin, Afshin Rostamizadeh, and Umar Syed. Learning Prices for Repeated Auctions with Strategic Buyers. In Advances in Neural Information Processing Systems, pages 1169-1177, 2013.
[7] Elliot Anshelevich, Anirban Dasgupta, Jon Kleinberg, Éva Tardos, Tom Wexler, and Tim Roughgarden. The price of stability for network design with fair cost allocation. SIAM Journal on Computing, 38(4):1602-1623, 2008.
[8] Susan Athey and Ilya Segal. An Efficient Dynamic Mechanism. *Econometrica*, 81(6):2463-2485, 2013.
[9] Avrim Blum, Nika Haghtalab, Richard Lanas Phillips, and Han Shao. One for one, or all for all: Equilibria and optimality of collaboration in federated learning. In International Conference on Machine Learning, pages 1005–1014. PMLR, 2021.
[10] Almut Burchard. A short course on rearrangement inequalities. Lecture notes, IMDEA Winter School, Madrid, 2009.
[11] Yang Cai, Constantinos Daskalakis, and Christos Papadimitriou. Optimum statistical estimation with strategic data sources. In Conference on Learning Theory, pages 280–296. PMLR, 2015.
[12] Yiding Chen, Xuezhou Zhang, Kaiqing Zhang, Mengdi Wang, and Xiaojin Zhu. Byzantine-robust online and offline distributed reinforcement learning. In International Conference on Artificial Intelligence and Statistics, pages 3230–3269. PMLR, 2023.
[13] Edward H Clarke. Multipart Pricing of Public Goods. Public Choice, 1971.
[14] Ilias Diakonikolas, Gautam Kamath, Daniel M Kane, Jerry Li, Ankur Moitra, and Alistair Stewart. Robust estimators in high dimensions without the computational intractability. In 2016 IEEE 57th Annual Symposium on Foundations of Computer Science (FOCS).
[15] Ningning Ding, Zhixuan Fang, and Jianwei Huang. Incentive mechanism design for federated learning with multi-dimensional private information. In 2020 18th International Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOPT), pages 1-8. IEEE, 2020.
[16] Mona Flores, Ittai Dayan, Holger Roth, Aoxiao Zhong, Ahmed Harouni, Amilcare Gentili, Anas Abidin, Andrew Liu, Anthony Costa, Bradford Wood, et al. Federated learning used for predicting outcomes in sars-cov-2 patients. Research Square, 2021.
[17] Yann Fraboni, Richard Vidal, and Marco Lorenzi. Free-rider attacks on model aggregation in federated learning. In International Conference on Artificial Intelligence and Statistics, pages 1846-1854. PMLR, 2021.
[18] Theodore Groves. Efficient Collective Choice when Compensation is Possible. The Review of Economic Studies, 1979.
[19] Ruoxi Jia, David Dao, Boxin Wang, Frances Ann Hubis, Nick Hynes, Nezihe Merve Gürel, Bo Li, Ce Zhang, Dawn Song, and Costas J Spanos. Towards efficient data valuation based on the shapley value. In The 22nd International Conference on Artificial Intelligence and Statistics, pages 1167-1176. PMLR, 2019.