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Designing an incentive compatible auction that maximizes expected revenue is an intricate task.
A neural architecture for designing truthful and efficient auctions
Tacchetti, A., Strouse, D., Garnelo, M., Graepel, T., and Bachrach, Y. (2019) · 1907
Earlier work this paper cites.
Tres observaciones sobre el algebra lineal
Birkhoff, G. (1946) · 1946
Earlier work this paper cites.
Straightforward individual incentive compatibility in large economies
Hammond, P. (1979) · 1979
Earlier work this paper cites.
Optimal auction design
Myerson, R. (1981) · 1981
Earlier work this paper cites.
Bundling decisions by a multiproduct monopolist with incomplete information
Palfrey, T. (1983) · 1983
Earlier work this paper cites.
A necessary and sufficient condition for rationalizability in a quasilinear context
Rochet, J.-C. (1987) · 1987
Earlier work this paper cites.
A Contribution to the Pure Theory of Taxation
Guesnerie, R. (1995) · 1995
Earlier work this paper cites.
Monotonic networks
Sill, J. (1998) · 1998
Earlier work this paper cites.
Bidding languages for combinatorial auctions
Boutilier, C. and Hoos, H. H. (2001) · 2001
Earlier work this paper cites.
Complexity of mechanism design
Conitzer, V. and Sandholm, T. (2002) · 2002
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Conitzer, V. and Sandholm, T. (2004) · 2004
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Ausubel, L., Cramton, P., and Milgrom, P. (2006) · 2006
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Manelli, A. and Vincent, D. (2006) · 2006
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Vorobeychik, Y., Kiekintveld, C., and Wellman, M. P. (2006) · 2006
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Wellman, M. P. (2006) · 2006
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Jehiel, P., Meyer-ter-Vehn, M., and Moldovanu, B. (2007) · 2007
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Kiekintveld, C. and Wellman, M. P. (2008) · 2008
Earlier work this paper cites.
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Anthony, M. and Bartlett, P. L. (2009) · 2009
Earlier work this paper cites.
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Rudin, C. and Schapire, R. E. (2009) · 2009
Earlier work this paper cites.
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Chawla, S., Hartline, J. D., Malec, D. L., and Sivan, B. (2010) · 2010
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Earlier work this paper cites.
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Guo, M. and Conitzer, V. (2010) · 2010
Earlier work this paper cites.
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Jordan, P. R., Schvartzman, L. J., and Wellman, M. P. (2010) · 2010
Earlier work this paper cites.
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A kernel-based iterative combinatorial auction
Lahaie, S. (2011) · 2011
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Optimal mechanism for selling two goods
Pavlov, G. (2011) · 2011
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Symmetries and optimal multi-dimensional mechanism design
Daskalakis, C. and Weinberg, S. M. (2012) · 2012
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., and Hinton, G. E. (2012) · 2012
Earlier work this paper cites.
Constrained automated mechanism design for infinite games of incomplete information
Vorobeychik, Y., Reeves, D. M., and Wellman, M. P. (2012) · 2012
Earlier work this paper cites.
Designing random allocation mechanisms: Theory and applications
Budish, E., Che, Y.-K., Kojima, F., and Milgrom, P. (2013) · 2013
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Understanding incentives: Mechanism design becomes algorithm design
Cai, Y., Daskalakis, C., and Weinberg, S. M. (2013) · 2013
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Mechanism design via optimal transport
Daskalakis, C., Deckelbaum, A., and Tzamos, C. (2013) · 2013
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A simple and approximately optimal mechanism for an additive buyer
Babaioff, M., Immorlica, N., Lucier, B., and Weinberg, S. M. (2014) · 2014
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The sample complexity of revenue maximization
Cole, R. and Roughgarden, T. (2014) · 2014
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Payment rules through discriminant-based classifiers
Dütting, P., Fischer, F., Jirapinyo, P., Lai, J., Lubin, B., and Parkes, D. C. (2014) · 2014
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Deep learning for revenue-optimal auctions with budgets
Feng, Z., Narasimhan, H., and Parkes, D. C. (2018) · 2018
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When is pure bundling optimal?
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Learning revenue-maximizing auctions with differentiable matching
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