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..
Learning-Augmented Facility Location Mechanisms for Envy Ratio
Authors: HARIS AZIZ, Yuhang Guo, Alexander Lam, Houyu Zhou
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | As a theoretical paper which lacks experiments, the paper clearly conforms to the Neur IPS Code of Ethics. |
| Researcher Affiliation | Academia | Haris Aziz UNSW Sydney EMAIL Yuhang Guo UNSW Sydney EMAIL Alexander Lam Hong Kong Polytechnic University EMAIL Houyu Zhou UNSW Sydney EMAIL |
| Pseudocode | Yes | Mechanism 1 α-Bounding Interval Mechanism (α-BIM) [...] Mechanism 2 (α, p)-LRM Constant Mechanism [...] Mechanism 3 Bias-Aware Mechanism (BAM) [...] Mechanism 4 α-Bounding Interval Randomized Mechanism [...] Mechanism 5 Bias-Aware LRM Mechanism |
| Open Source Code | No | The paper does not include experiments or code. |
| Open Datasets | No | The paper does not include experiments. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |