Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Worst-Case VCG Redistribution Mechanism Design Based on the Lottery Ticket Hypothesis
Authors: Mingyu Guo
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The results are summarized in Table 1. The quality of training is naturally the gap between the achieved worst-case allocative efficiency ratio and the theoretical upper bound. We use WCT to denote our worst-case training algorithm. |
| Researcher Affiliation | Academia | School of Computer and Mathematical Sciences University of Adelaide, Australia EMAIL |
| Pseudocode | Yes | Algorithm 1: Worst-Case Training Algorithm |
| Open Source Code | No | The paper does not provide any statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper does not use pre-existing public datasets; instead, it generates 'random type profiles' and 'worst-case type profiles' as training samples. No links or citations for publicly available datasets are provided. |
| Dataset Splits | No | The paper describes how training batches are composed of generated type profiles but does not specify train/validation/test splits of a fixed dataset or reference standard split methodologies for reproducibility. |
| Hardware Specification | Yes | The hardware allocated to each job is 1 CPU core from Intel Xeon Platinum 8360Y (for running MIPs) and 1 GPU core from Nvidia A100 (for neural network training). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., specific libraries, frameworks, or solvers with their versions). |
| Experiment Setup | Yes | Run Adam SGD on h with learning rate 0.0001 for 500 epochs. Training batch consists of: 16 latest calculated worst-case type profiles (i.e., WCP[-16:]) 16 randomly sampled worst-case type profiles from earlier (i.e., from WCP[:-16]) 16 random type profiles n + 1 type profiles where the agents either report 1 n/2 or 0 (i.e., type profiles for deriving the conjectured upper bound (Naroditskiy et al. 2012)) |