[7]
A. Beattiea, The story behind Google’s success, https://www.investopedia.com/articles/personal-finance/042415/story-behind-googles-success.asp, 2018.
[9]
C. Guo, Z. Huang, and X. Zhang, Settling the sample complexity of single-parameter revenue maximization, in Proc. 51st Annu. ACM SIGACT Symp. Theory of Computing, Phoenix, AZ, USA, 2019, pp. 662–673.
[10]
C. Guo, Z. Huang, Z. G. Tang, and X. Zhang, Generalizing complex hypotheses on product distributions: Auctions, prophet inequalities, and Pandora’s problem, in Proc. 34th Annu. Conf. Learning Theory, Boulder, CO, USA, 2019, pp. 2248–2288.
[12]
P. Tang and Y. Zeng, The price of prior dependence in auctions, in Proc. 2018 ACM Conf. Economics and Computation, Ithaca, NY, USA, 2018, pp. 485–502.
[13]
S. Lahaie, D. Pennock, A. Saberi, and R. Vohra, Sponsored search auctions, in Algorithmic Game Theory, N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani Eds. Cambridge, UK: Cambridge University Press, 2007, pp. 699–716.
[16]
R. P. Leme and E. Tardos, Pure and Bayes–Nash price of anarchy for generalized second price auction, in Proc. 2010 IEEE 51st Ann. Symp. Foundations of Computer Science, Las Vegas, NV, USA, 2010, pp. 735–744.
[17]
B. Lucier and R. P. Leme, GSP auctions with correlated types, in Proc. 12th ACM Conf. Electronic Commerce, San Jose, CA, USA, 2011, pp. 71–80.
[18]
I. Caragiannis, C. Kaklamanis, P. Kanellopoulos, and M. Kyropoulou, On the efficiency of equilibria in generalized second price auctions, in Proc. 12th ACM Conf. Electronic Commerce, San Jose, CA, USA, 2011, pp. 81–90.
[21]
B. Lucier, R. P. Leme, and E. Tardos, On revenue in the generalized second price auction, in Proc. 21st Int. Conf. World Wide Web, Lyon, France, 2012, pp. 361–370.
[22]
R. Cole and T. Roughgarden, The sample complexity of revenue maximization, in Proc. forty-sixth annual ACM Symp. Theory of Computing, New York, New York, 2014, pp. 243–252.
[23]
V. Syrgkanis, A sample complexity measure with applications to learning optimal auctions, in Proc. 31st Int. Conf. Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 5358–5365.
[25]
M. F. F. Balcan, T. Sandholm, and E. Vitercik, Sample complexity of automated mechanism design, in Proc. 30th Conf. Neural Information Processing Systems (NIPS 2016), Barcelona, Spain, 2016, pp. 2083–2091.
[26]
Y. Cai and C. Daskalakis, Learning multi-item auctions with (or without) samples, in Proc. 2017 IEEE 58th Annu. Symp. Foundations of Computer Science (FOCS), Berkeley, CA, USA, 2017, pp. 516–527.
[27]
M. F. Balcan, T. Sandholm, and E. Vitercik, A general theory of sample complexity for multi-item profit maximization, in Proc. 2018 ACM Conf. Economics and Computation, Ithaca, NY, USA, 2018, pp. 173–174.
[30]
H. Nazerzadeh, A. Saberi, and R. Vohra, Dynamic cost-per-action mechanisms and applications to online advertising, in Proc. 17th Int. Conf. World Wide Web, Beijing, China, 2008, pp. 179–188.
[32]
N. R. Devanur and S. M. Kakade, The price of truthfulness for pay-per-click auctions, in Proc. 10th ACM Conf. Electronic Commerce, Stanford, CA, USA, 2009, pp. 99–106.
[33]
M. Babaioff, R. D. Kleinberg, and A. Slivkins, Truthful mechanisms with implicit payment computation, in Proc. 11th ACM Conf. Electronic Commerce, New York, NY, USA, 2010, pp. 43–52.
[34]
K. Amin, A. Rostamizadeh, and U. Syed, Learning prices for repeated auctions with strategic buyers, in Proc. 26th Int. Conf. Neural Information Processing Systems, Lake Tahoe, NV, USA, 2013, pp. 1169–1177.
[35]
K. Amin, A. Rostamizadeh, and U. Syed, Repeated contextual auctions with strategic buyers, in Proc. 27th Int. Conf. Neural Information Processing Systems, Montreal, Canada, 2014, pp. 622–630.
[36]
M. Braverman, J. Mao, J. Schneider, and M. Weinberg, Selling to a no-regret buyer, in Proc. 2018 ACM Conf. Economics and Computation, Ithaca, NY, USA, 2018, pp. 523–538.
[37]
Z. Huang, J. Liu, and X. Wang, Learning optimal reserve price against non-myopic bidders, in Proc. 32nd Conf. Neural Information Processing Systems (NIPS 2018), Montreal, Canada, 2018, pp. 2042–2052.
[38]
J. D. Abernethy, R. Cummings, B. Kumar, S. Taggart, and J. H. Morgenstern, Learning auctions with robust incentive guarantees, in Proc. 33rd Conf. Neural Information Processing Systems (NeurIPS 2019), Vancouver, Canada, 2019, pp. 11587–11597.
[39]
X. Deng, R. Lavi, T. Lin, Q. Qi, W. Wang, and X. Yan, A game-theoretic analysis of the empirical revenue maximization algorithm with endogenous sampling, in Proc. 34th Conf. Neural Information Processing Systems (NeurIPS 2020) virtual, 2020.
[42]
T. Nedelec, N. El Karoui, and V. Perchet, Learning to bid in revenue maximizing auction, in Proc. The 2019 World Wide Web Conference, San Francisco, USA, 2019, pp. 934–935.
[44]
M. Hardt, N. Megiddo, C. Papadimitriou, and M. Wootters, Strategic classification, in Proc. 2016 ACM Conf. Innovations in Theoretical Computer Science, Cambridge, MA, USA, 2016, pp. 111–122.
[45]
Y. Chen, C. Podimata, A. D. Procaccia, and N. Shah, Strategyproof linear regression in high dimensions, in Proc. 2018 ACM Conf. Economics and Computation, New York, NY, USA, 2018, pp. 9–26.
[47]
J. D. Hartline, Optimal mechanisms, in Mechanism Design and Approximation, https://jasonhartline.com/MDnA/, 2013.
[48]
S. Chawla and J. D. Hartline, Auctions with unique equilibria, in Proc. fourteenth ACM Conf. Electronic Commerce, 2013, pp. 181–196.
[49]
N. Nisan, Introduction to mechanism design (for computer scientists), in Algorithmic Game Theory, N. Nisan, T. Roughgarden, E. Tardos, and V. Vazirani Eds. Cambridge, UK: Cambridge University Press, 2007, pp. 209–242.
[52]
Z. Chen, X. Deng, J. Li, C. Wang, and M. Yang, Budget-constrained auctions with unassured priors, arXiv preprint arXiv: 2203.16816, 2022.
[53]
Y. Chen, X. Deng, and Y. Li, Optimal private payoff manipulation against commitment in extensive-form games, arXiv preprint arXiv: 2206.13119, 2022.