Name
Abstract | ||
|---|---|---|
The problem of market clearing is to set a price for an item such that quantity demanded equals quantity supplied. In this work, we cast the problem of predicting clearing prices into a learning framework and use the resulting models to perform revenue optimization in auctions and markets with contextual information. The economic intuition behind market clearing allows us to obtain fine-grained control over the aggressiveness of the resulting pricing policy, grounded in theory. To evaluate our approach, we fit a model of clearing prices over a massive dataset of bids in display ad auctions from a major ad exchange. The learned prices outperform other modeling techniques in the literature in terms of revenue and efficiency trade-offs. Because of the convex nature of the clearing loss function, the convergence rate of our method is as fast as linear regression. |
Year | Venue | Field |
|---|---|---|
2019 | international conference on machine learning | Revenue,Econometrics,Mathematical optimization,Contextual information,Market clearing,Clearing,Intuition,Common value auction,Rate of convergence,Mathematics,Linear regression |
DocType | Volume | Citations |
Journal | abs/1906.01184 | 0 |
PageRank | References | Authors |
0.34 | 0 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Weiran Shen | 1 | 5 | 8.25 |
| Sébastien Lahaie | 2 | 361 | 35.37 |
| renato paes | 3 | 331 | 36.45 |