Learning Multi-Item Auctions with (or without) Samples
© 2017 IEEE. We provide algorithms that learn simple auctions whose revenue is approximately optimal in multi-item multi-bidder settings, for a wide range of bidder valuations including unit-demand, additive, constrained additive, XOS, and subadditive. We obtain our learning results in two settings....
| Main Authors: | , |
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| Other Authors: | , |
| Format: | Article |
| Language: | English |
| Published: |
Institute of Electrical and Electronics Engineers (IEEE),
2021-11-05T15:13:17Z.
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| Subjects: | |
| Online Access: | Get fulltext |