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..
Polynomial-Time Computation of Exact $\Phi$-Equilibria in Polyhedral Games
Authors: Gabriele Farina, Charilaos Pipis
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Since this is a purely theoretical paper, exploring the tractability of computing high-precision equilibria in games (and giving a polynomial-time algorithm of rather high complexity), it does not include any experiments. |
| Researcher Affiliation | Academia | Gabriele Farina MIT EMAIL Charilaos Pipis MIT EMAIL |
| Pseudocode | Yes | Algorithm 1: Ellipsoid Against Hope for bilinear zero-sum games |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | Since this is a purely theoretical paper, exploring the tractability of computing high-precision equilibria in games (and giving a polynomial-time algorithm of rather high complexity), it does not include any 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. Therefore, it does not specify any software dependencies for experimental execution. |
| Experiment Setup | No | The paper does not include experiments. |