A Context-Integrated Transformer-Based Neural Network for Auction Design
Authors: Zhijian Duan, Jingwu Tang, Yutong Yin, Zhe Feng, Xiang Yan, Manzil Zaheer, Xiaotie Deng
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct empirical experiments to show the effectiveness of CITrans Net in different contextual auctions... We present the experimental results of Setting A, B and C in Table 1. |
| Researcher Affiliation | Collaboration | 1Peking University, Beijing, China 2Google Research, Mountain View, US 3Shanghai Jiao Tong University, Shanghai, China 4Google Deep Mind, Mountain View, US. |
| Pseudocode | Yes | Algorithm 1 describe the training procedure of CITrans Net. |
| Open Source Code | Yes | Our implementation is available at https://github. com/zjduan/CITrans Net. |
| Open Datasets | No | For all the settings (Setting A-I), we generate the training set of each setting with size in {50000, 100000, 200000} and test set of size 5000. |
| Dataset Splits | No | For all the settings (Setting A-I), we generate the training set of each setting with size in {50000, 100000, 200000} and test set of size 5000. |
| Hardware Specification | No | Our experiments are run on a Linux machine with NVIDIA Graphics Processing Unit (GPU) cores. |
| Software Dependencies | No | All the models and regret are optimized through Adam (Kingma & Ba, 2014) optimizer. |
| Experiment Setup | Yes | We set the embedding size in settings with discrete context (Setting A, B, D, E, F) as 16. The value of ρ in the augmented Lagrangian (Equation (22)) was set as 1.0 at the beginning and incremented by 5 every two epochs. The value of λ in Equation (22) was set as 5.0 initially and incremented every certain number (selected from {2 10}) of epochs... For our proposed CITrans Net, the output channel of the first 1 1 convolution in both the input layer and interaction layers are set to 64. We set d = 64 for the 1 1 convolution with residual connection in input layer, and dh = 64 for the final 1 1 convolution in each interaction layer. We tune the numbers of interaction layers from {2, 3}, and in each interaction layer we adopt transformer with 4 heads and 64 hidden nodes. |