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..
On Tractable $\Phi$-Equilibria in Non-Concave Games
Authors: Yang Cai, Constantinos Daskalakis, Haipeng Luo, Chen-Yu Wei, Weiqiang Zheng
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we initiate the study of tractable Φ-equilibria in nonconcave games and examine several natural families of strategy modifications. We show that when Φ is finite, there exists an efficient uncoupled learning algorithm that approximates the corresponding Φ-equilibria. Additionally, we explore cases where Φ is infinite but consists of local modifications, showing that Online Gradient Descent can efficiently approximate Φ-equilibria in non-trivial regimes. |
| Researcher Affiliation | Academia | Yang Cai Yale University EMAIL Constantinos Daskalakis MIT CSAIL EMAIL Haipeng Luo University of Southern California EMAIL Chen-Yu Wei University of Virginia EMAIL Weiqiang Zheng Yale University EMAIL |
| Pseudocode | Yes | Algorithm 1: Φ-regret minimization for non-concave reward via sampling; Algorithm 2: SAMPLESTRATEGY; Algorithm 3: Conv(Φ)-regret minimization for Lipschitz smooth non-concave rewards |
| Open Source Code | No | The paper does not contain any explicit statement about providing open-source code for the methodology described, nor does it provide a link to a code repository. |
| Open Datasets | No | This paper does not contain experimental results and therefore does not use a dataset. |
| Dataset Splits | No | This paper does not contain experimental results and therefore does not define dataset splits. |
| Hardware Specification | No | This paper does not contain experimental results and therefore does not provide hardware specifications. |
| Software Dependencies | No | This paper does not contain experimental results and therefore does not list specific software dependencies with version numbers. |
| Experiment Setup | No | This paper does not contain experimental results and therefore does not provide details on experimental setup or hyperparameters. |