[2] Nicolo Cesa-Bianchi, Yoav Freund, David Haussler, David P Helmbold, Robert E Schapire, and Manfred K Warmuth. How to use expert advice. Journal of the ACM (JACM), 44(3):427-485, 1997.
[3] Nick Littlestone and Manfred K Warmuth. The weighted majority algorithm. Information and computation, 108(2):212-261, 1994.
[4] Jyrki Kivinen and Manfred K Warmuth. Averaging expert predictions. In European Conference on Computational Learning Theory, pages 153-167. Springer, 1999.
[5] Philip E Tetlock and Dan Gardner. Superforecasting: The art and science of prediction. Random House, 2016.
[6] Manfred K Warmuth and Dima Kuzmin. Randomized online pca algorithms with regret bounds that are logarithmic in the dimension. Journal of Machine Learning Research, 9(Oct):2287-2320, 2008.
[7] Wouter M Koolen, Manfred K Warmuth, Jyrki Kivinen, et al. Hedging structured concepts. In  $COLT$ , pages 93-105. CiteSeer, 2010.
[8] Alon Cohen and Tamir Hazan. Following the perturbed leader for online structured learning. In International Conference on Machine Learning, pages 1034–1042. PMLR, 2015.
[9] Samrat Mukhopadhyay, Sourav Sahoo, and Abhishek Sinha. k-experts-online policies and fundamental limits. In International Conference on Artificial Intelligence and Statistics, pages 342-365. PMLR, 2022.
[10] Rupert Freeman, David M Pennock, Chara Podimata, and Jennifer Wortman Vaughan. No-regret and incentive-compatible online learning. arXiv preprint arXiv:2002.08837, 2020.
[11] Tim Roughgarden and Okke Schrijvers. Online prediction with selfish experts. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 1300-1310. Curran Associates, Inc., 2017.
[12] Rafael Frongillo, Robert Gomez, Anish Thilagar, and Bo Waggoner. Efficient competitions and online learning with strategic forecasters. In Proceedings of the 22nd ACM Conference on Economics and Computation, pages 479-496, 2021.
[13] Nicholas Harvey, Christopher Liaw, and Tasuku Soma. Improved algorithms for online submodular maximization via first-order regret bounds. Advances in Neural Information Processing Systems, 33:123-133, 2020.
[14] Michele Conforti and Gérard Cornuéjols. Submodular set functions, matroids and the greedy algorithm: tight worst-case bounds and some generalizations of the rado-edmonds theorem. Discrete applied mathematics, 7(3):251-274, 1984.
[15] Mark D Reid and Robert C Williamson. Surrogate regret bounds for proper losses. In Proceedings of the 26th Annual International Conference on Machine Learning, pages 897-904, 2009.
[16] Thodoris Lykouris and Sergei Vassilvitskii. Competitive caching with machine learned advice. Journal of the ACM (JACM), 68(4):1-25, 2021.
[17] Nicolas S Lambert, John Langford, Jennifer Wortman, Yiling Chen, Daniel Reeves, Yoav Shoham, and David M Penno k. Self-financed wagering mechanisms for forecasting. In Proceedings of the 9th ACM conference on Electronic commerce, pages 170-179, 2008.
[18] Nicolas S Lambert, John Langford, Jennifer Wortman Vaughan, Yiling Chen, Daniel M Reeves, Yoav Shoham, and David M Pennock. An axiomatic characterization of wagering mechanisms. Journal of Economic Theory, 156:389-416, 2015.
[19] Sanjeev Arora, Elad Hazan, and Satyen Kale. The multiplicative weights update method: a meta-algorithm and applications. Theory of computing, 8(1):121-164, 2012.
[20] Jacob D Abernethy, Chansoo Lee, and Ambuj Tewari. Fighting bandits with a new kind of smoothness. Advances in Neural Information Processing Systems, 28, 2015.
[21] Matthew Streeter and Daniel Golovin. An online algorithm for maximizing submodular functions. Advances in Neural Information Processing Systems, 21, 2008.
[22] Moran Feldman. Guess free maximization of submodular and linear sums. Algorithmica, 83(3):853-878, 2021.