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].
Fast Rates in Time-Varying Strongly Monotone Games
Authors: Yu-Hu Yan, Peng Zhao, Zhi-Hua Zhou
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5. Experiment This section provides empirical evaluations of our proposed method in time-varying strongly monotone games. [...] Results. We report average results with standard deviations of 5 independent runs. [...] Figure 1 plots the tracking error of all methods. Smaller tracking error indicates better performance. The results show the supremacy of our TV-Smog and TV-Smog (Smooth), supporting our theoretical results. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China. Correspondence to: Peng Zhao <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 TV-SMOG for the i-th player, Algorithm 2 TV-SMOG (smooth) for the i-th player, Algorithm 3 Deploying ADER (Zhang et al., 2018) for the i-th player, Algorithm 4 FLH-OGD (Hazan & Seshadhri, 2007), Algorithm 5 Known strong monotonicity parameter µ and box domain (Baby & Wang, 2022), Algorithm 6 Known strong monotonicity parameter µ (Baby & Wang, 2022). |
| Open Source Code | No | The paper does not contain any statement about open-source code release or provide links to a code repository for its methodology. |
| Open Datasets | Yes | We use four LIBSVM datasets to initialize the logistic loss. |
| Dataset Splits | No | The paper does not provide specific training/test/validation dataset splits, percentages, or absolute sample counts. |
| Hardware Specification | No | The paper does not explicitly describe the hardware (e.g., specific GPU or CPU models, cloud resources) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers (e.g., libraries, frameworks, or programming language versions). |
| Experiment Setup | Yes | We set µ = 0.005, i.e., ut is 0.01-strongly monotone. [...] We focus on the standard linear model: P(x) a b PN i=1 xi, where a = 0.5 and b = 1/(NR). [...] We consider time-varying zero-sum strongly convex-concave games with utility ut(x, y) 1 2 x 2 + x Aty 1 2 y 2, which is 1-strongly monotone and 1-smooth. [...] All hyper-parameters are set to be theoretically optimal. |