Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
No-Regret Learning in Time-Varying Zero-Sum Games
Authors: Mengxiao Zhang, Peng Zhao, Haipeng Luo, Zhi-Hua Zhou
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 42,240 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,635 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical results further validate the effectiveness of our algorithm. We also conduct empirical studies to further support our theoretical findings. |
| Researcher Affiliation | Academia | 1University of Southern California 2National Key Laboratory for Novel Software Technology, Nanjing University. |
| Pseudocode | Yes | Algorithm 1 Algorithm for the x-player |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | We construct an environment such that PT = Θ(T), WT = Θ(T 3 4 ), and VT = Θ(T). |
| Dataset Splits | No | The paper describes a simulated environment and does not mention explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper mentions implementing the algorithm but does not specify software names with version numbers for reproducibility. |
| Experiment Setup | Yes | We set the size of game matrix to be m n with m = 2 and n = 2. The total time horizon is set as T = 2 106. [...] We implement Algorithm 1 for x-player and Algorithm 2 for y-player with L = 4 and step size pool ηi = 2i 1 T for both players. The number of base-learners (i.e., the size of step size pool) is N = 1 2 log2 T + 1 = 11. |