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..
Doubly Optimal No-Regret Learning in Monotone Games
Authors: Yang Cai, Weiqiang Zheng
ICML 2023 | Venue PDF | 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. |