Certifying Strategyproof Auction Networks
Authors: Michael Curry, Ping-yeh Chiang, Tom Goldstein, John Dickerson
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We experiment on two auction settings: 1 agent, 2 items, with valuations uniformly distributed on [0, 1] (the true optimal mechanism is derived analytically and presented by Manelli and Vincent [2006]); and 2 agents, 2 items, with valuations uniformly distributed on [0, 1], which is unsolved analytically but shown to be empirically learnable in Duetting et al. [2019]. For each of these settings, we train 3 networks: ... Our results for regret, revenue and solve time are summarized in Table 1. |
| Researcher Affiliation | Academia | Michael J. Curry curry@cs.umd.edu Computer Science Department University of Maryland College Park, MD 20742; Ping-Yeh Chiang pchiang@cs.umd.edu Computer Science Department University of Maryland College Park, MD 20742; Tom Goldstein tomg@cs.umd.edu Computer Science Department University of Maryland College Park, MD 20742; John P. Dickerson john@cs.umd.edu Computer Science Department University of Maryland College Park, MD 20742 |
| Pseudocode | No | The paper describes methods and equations, but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks, nor does it present structured steps in a code-like format. |
| Open Source Code | No | The paper states 'All training code is implemented using the Py Torch framework Paszke et al. [2019]' but does not provide a link to its own open-source implementation or explicitly state that the code for the described methodology is publicly available. |
| Open Datasets | No | The paper states 'We generate 600,000 valuation profiles as training set and 3,000 valuation profiles as the testing set' and describes valuations as 'uniformly distributed on [0, 1]', indicating synthetically generated data without providing a link, DOI, or citation for public access to the specific generated dataset used. |
| Dataset Splits | Yes | We generate 600,000 valuation profiles as training set and 3,000 valuation profiles as the testing set. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'All training code is implemented using the Py Torch framework Paszke et al. [2019]' and refers to a 'Gurobi-based Gurobi Optimization, LLC [2020] integer program formulation', but does not provide specific version numbers for PyTorch, Gurobi, or any other software dependencies. |
| Experiment Setup | Yes | We use a batch size of 20,000 for training, and we train the network for a total of 1000 epochs. At train time, we generate misreports through 25 steps of gradient ascent on the truthful valuation profiles with learning rate of .02; at test time, we use 1000 steps. ... detailed architectures, along with hyperparameters of the augmented Lagrangian, are reported in Appendix ??. |