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 [1].
Learning Nash Equilibria in Rank-1 Games
Authors: Nikolas Patris, Ioannis Panageas
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | C EXPERIMENTS In this section, we provide experimental evaluation supporting our theoretical finding. Here we restrict to a single example, while we also provide an anonymous repository containing multiple rank-1 games of multiple sizes |
| Researcher Affiliation | Academia | Nikolas Patris University of California, Irvine EMAIL Ioannis Panageas University of California, Irvine EMAIL |
| Pseudocode | Yes | Algorithm 1: Rank-1 Game Solver [...] Algorithm 2: OMWU(x, y, λ) |
| Open Source Code | Yes | C EXPERIMENTS In this section, we provide experimental evaluation supporting our theoretical finding. Here we restrict to a single example, while we also provide an anonymous repository containing multiple rank-1 games of multiple sizes |
| Open Datasets | No | We constructed a random 6 6 game defined by the matrix A, and the vectors a, b. The paper constructs a specific example for experiments but does not provide access details for a publicly available or open dataset. |
| Dataset Splits | No | The paper constructs a specific 6x6 game for its experiments but does not provide any information about training, validation, or test splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'optimistic gradient ascent (OGA)' but does not provide specific software names with version numbers. |
| Experiment Setup | No | The paper provides theoretical bounds for learning rates and iterations but does not specify concrete hyperparameter values or detailed training configurations (e.g., specific learning rates used in experiments, batch sizes, number of epochs) for the experimental setup. |