Learning Optimal Auctions with Correlated Value Distributions

The correlation of values commonly exists in auctions, which can be further exploited to improve revenue. However, the complex correlation structure makes it hard to manually design the optimal auction mechanism. Data-driven auction mechanisms, powered by machine learning, enable to design auctions directly from historical auction data, without relying on specific value distributions. In this work, we synthesize the learning-based auction and the characteristics of strategy-proofness in the correlated value setting, and propose a new auction mechanism, namely Conditional Auction Net (CAN). The CAN can encode the correlation of values into the rank score of each bidder, and further adjust the allocation rule to approach the optimal revenue. The property of strategy-proofness is guaranteed by encoding the game theoretical condition into the neural network structure. Furthermore, all operations in the designed auctions are differentiable to enable an end-to-end training paradigm. We also present CAN can provide a large solution space to adequately encode the correlation of values. Experimental results demonstrate that the proposed auction mechanism can represent almost any strategy-proof auction mechanism, and outperforms the auction mechanisms wildly used in the correlated value settings.