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..
Randomized Strategic Facility Location with Predictions
Authors: Eric Balkanski, Vasilis Gkatzelis, Golnoosh Shahkarami
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we provide a deeper understanding of this problem by exploring the power of randomization as well as the impact of different types of predictions on the performance of truthful learning-augmented mechanisms. We study both the single-dimensional and the Euclidean case and provide upper and lower bounds regarding the achievable approximation of the optimal egalitarian social cost. [...] The contributions of our paper are mainly theoretical. |
| Researcher Affiliation | Academia | Eric Balkanski Columbia University, IEOR EMAIL Vasilis Gkatzelis Drexel University, Computer Science EMAIL Golnoosh Shahkarami Max Planck Institut für Informatik, Universität des Saarlandes EMAIL |
| Pseudocode | Yes | Mechanism 1: Centroid Mechanism on Extreme Agents Input :Location profile x = x1, , xn , Predictions ˆe = e1, , ek Output :Probability distribution P on location of the facility With probability 1/2: return the centroid G = xe1+ +xek k With probability 1/2k: return each point xe1, , xek |
| Open Source Code | No | The contributions of our paper are mainly theoretical. (from Neur IPS checklist question 5 justification) |
| Open Datasets | No | The contributions of our paper are mainly theoretical. (from Neur IPS checklist question 4, 5, 6, 7, 8 justifications) |
| Dataset Splits | No | The contributions of our paper are mainly theoretical. (from Neur IPS checklist question 4, 5, 6, 7, 8 justifications) |
| Hardware Specification | No | The contributions of our paper are mainly theoretical. (from Neur IPS checklist question 8 justification) |
| Software Dependencies | No | The contributions of our paper are mainly theoretical. (from Neur IPS checklist question 5 justification) |
| Experiment Setup | No | The contributions of our paper are mainly theoretical. (from Neur IPS checklist question 6 justification) |