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We study online learning problems in which a decision maker has to make a sequence of costly decisions, with the goal of maximizing their expected reward while adhering to budget and return-on-investment (ROI) constraints.
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Badanidiyuru, A., Langford, J., and Slivkins, A · 2014
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Online primal-dual mirror descent under stochastic constraints
Wei, X., Yu, H., and Neely, M. J · 2020
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Distributed online convex optimization with time-varying coupled inequality constraints
Yi, X., Li, X., Xie, L., and Johansson, K. H · 2020
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Non-quasi-linear agents in quasi-linear mechanisms
Babaioff, M., Cole, R., Hartline, J., Immorlica, N., and Lucier, B · 2021
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The landscape of auto-bidding auctions: Value versus utility maximization
Balseiro, S. R., Deng, Y., Mao, J., Mirrokni, V. S., and Zuo, S · 2021
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Kumar, R. and Kleinberg, R · 2022
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Nedelec, T., Calauzènes, C., El Karoui, N., Perchet, V., et al · 2022
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