Learning to bid in revenue-maximizing auctions
Authors: Thomas Nedelec, Noureddine El Karoui, Vianney Perchet
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our study is done in the setting where one bidder is strategic. Using a variational approach, we study the complexity of the original objective and we introduce a relaxation of the objective functional in order to use gradient descent methods. Our approach is simple, general and can be applied to various value distributions and revenuemaximizing mechanisms. The new strategies we derive yield massive uplifts compared to the traditional truthfully bidding strategy. ... We numerically optimize this new objective through a simple neural network and get very significant improvements in bidder utility compared to truthful bidding. |
| Researcher Affiliation | Collaboration | 1Criteo AI Lab 2CMLA, ENS Paris Saclay 3UC, Berkeley. Correspondence to: Thomas Nedelec <nedelec@cmla.enscachan.fr>. |
| Pseudocode | Yes | Algorithm 1 Boosted second price (r, γ) |
| Open Source Code | Yes | We finally provide the code in Py Torch that has been used to run the different experiments. ... The full code in Py Torch is provided with the paper. |
| Open Datasets | No | The paper mentions using |
| Dataset Splits | No | The paper mentions |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The paper mentions |
| Experiment Setup | Yes | To fit the optimal strategies, we use a simple one-layer neural network with 200 Re Lus. We replace the indicator function by a sigmoid function to have a fully differentiable objective and we optimize Uη(βi) = E (Xi hβi(Xi))Gi(β(Xi))σ(ηhβi(Xi)) . with σ(x) = 1 1+exp( x) and η = 1000. We start with a batch size of 10000 examples, sampled according to the value distribution of the bidder. We use a stochastic gradient algorithm (SGD) with a decreasing learning rate starting at 0.001. |