Doubly Optimal No-Regret Learning in Monotone Games
Authors: Yang Cai, Weiqiang Zheng
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we numerically verify our theoretical results through Example 1. The numerical result is shown in Figure 1. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Yale University, New Haven, USA. |
| Pseudocode | Yes | Algorithm 1 AOG with step-size adaptation |
| Open Source Code | Yes | The code can be found at https://github.com/weiqiangzheng1999/Doubly-Optimal-No-Regret-Learning. |
| Open Datasets | No | We consider a convex-concave min-max optimization problem minx X maxy Y f(x, y), which is also a two-player zero-sum game with f 1 = f 2 = f. Details of the choices of H, A, b, h, X, Y and step size η are deferred to Appendix F. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce data partitioning was found. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running experiments were found. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment were found. |
| Experiment Setup | Yes | We choose n = 100, X = Y = [-200, 200]^n. We run both AOG and OG with step size η = 0.3 and initial points x1 = y1 = 1/sqrt(n)1 for 10^5 iterations. |