A Scalable Neural Network for DSIC Affine Maximizer Auction Design
Authors: Zhijian Duan, Haoran Sun, Yurong Chen, Xiaotie Deng
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct extensive experiments to demonstrate that AMenu Net outperforms strong baselines in both contextual and non-contextual multi-item auctions, scales well to larger auctions, generalizes well to different settings, and identifies useful deterministic allocations. |
| Researcher Affiliation | Academia | Zhijian Duan CFCS, School of Computer Science Peking University zjduan@pku.edu.cn Haoran Sun Peking University sunhaoran0301@stu.pku.edu.cn Yurong Chen CFCS, School of Computer Science Peking University chenyurong@pku.edu.cn Xiaotie Deng CFCS, School of Computer Science & CMAR, Institute for AI Peking University xiaotie@pku.edu.cn |
| Pseudocode | No | The paper describes the architecture and steps of AMenu Net but does not provide formal pseudocode or an algorithm block. |
| Open Source Code | Yes | Our implementation is available at https://github.com/Haoran0301/AMenu Net |
| Open Datasets | No | We generate each bidder representations xi R10 and item representations yj R10 independently from a uniform distribution in [ 1, 1]10 (i.e., U[ 1, 1]10). The valuation vij is sampled from U[0, Sigmoid(x T i yj)]. |
| Dataset Splits | No | The paper mentions training samples and evaluation samples but does not explicitly describe a separate validation set or split for hyperparameter tuning. |
| Hardware Specification | No | All experiments are run on a Linux machine with NVIDIA Graphics Processing Unit (GPU) cores. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software dependencies, libraries, or solvers used in the experiments. |
| Experiment Setup | Yes | We train the models for a maximum of 8000 iterations, with 32768 generated samples per iteration. The batch size is 2048, and we evaluate all models on 100000 samples. We set the softmax temperature as 500 and the learning rate as 3 10 4. We tune the menu size in {32, 64, 128, 256, 512, 1024}. For the boost layer, we use a two-layer fully connected neural network with Re LU activation. The menu size and τ varies in different settings, and we present these numbers in 3. |