Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
On Discrete Truthful Heterogeneous Two-Facility Location
Authors: Panagiotis Kanellopoulos, Alexandros A. Voudouris, Rongsen Zhang
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 15,931 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,831 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we focus exclusively on deterministic mechanisms, and improve upon the bounds of Sera๏ฌno and Ventre for both the social and the maximum cost. Our goal is to design strategyproof mechanisms with an as low f-approximation ratio as possible (close to 1). The paper presents theorems, lemmas, and approximation ratios, which are characteristics of theoretical research without empirical data analysis. |
| Researcher Affiliation | Academia | Panagiotis Kanellopoulos , Alexandros A. Voudouris , Rongsen Zhang School of Computer Science and Electronic Engineering, University of Essex, UK EMAIL |
| Pseudocode | Yes | Mechanism 1: FIXED-OR-MEDIAN-NEAREST-EMPTY (FMNE) Mechanism 2: PRIORITY-DICTATORSHIP Mechanism 3: ฮฑ-LEFT-RIGHT |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | No | As a theoretical paper, it does not use empirical datasets for training. It discusses abstract problem instances for theoretical analysis, not real-world datasets. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, so there are no dataset splits for training, validation, or testing mentioned. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments requiring specific hardware, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments requiring specific software, so no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe empirical experiments with hyperparameters or system-level training settings. |