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..
Learning from Delayed Feedback in Games via Extra Prediction
Authors: Yuma Fujimoto, Kenshi Abe, Kaito Ariu
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The theoretical results are supported and strengthened by our experiments. Our experiments (Figs. 1-3) also support and reinforce these corollaries. |
| Researcher Affiliation | Industry | Yuma Fujimoto Cyber Agent EMAIL Kenshi Abe Cyber Agent EMAIL Kaito Ariu Cyber Agent EMAIL |
| Pseudocode | Yes | Algorithm 3 (Vanilla FTRL with time delay). When mt i = 0, generalized FTRL corresponds to vanilla FTRL. Algorithm 4 (Optimistic FTRL with time delay). When mt i = ut m i , generalized FTRL corresponds to optimistic FTRL (OFTRL). Algorithm 8 (Weighted Optimistic Follow the Regularized Leader). Weighted Optimistic Follow The Regularized Leader (WOFTRL) is given by mt i = nut m i for n N in generalized FTRL. |
| Open Source Code | Yes | The codes are available at https://github.com/CyberAgentAILab/delayed_learning_games |
| Open Datasets | No | The paper uses well-known theoretical game setups (Matching Pennies, Sato's Game, Rock-Paper-Scissors) for simulations, which do not involve external datasets in the traditional sense. |
| Dataset Splits | No | The paper conducts simulations of theoretical games (Matching Pennies, Sato's Game, Rock-Paper-Scissors) and therefore does not use external datasets requiring explicit train/test/validation splits. |
| Hardware Specification | Yes | Operating System: mac OS Monterey (version 12.4) Programming Language: Python 3.11.3 Processor: Apple M1 Pro (10 cores) Memory: 32 GB |
| Software Dependencies | Yes | Operating System: mac OS Monterey (version 12.4) Programming Language: Python 3.11.3 |
| Experiment Setup | Yes | A. The phase diagram of social regrets for various time delays m (horizontal) and optimistic weights n (vertical). ... We set the parameters as T = 105 and η = 10 2. ... B. The convergence of social regrets for various optimistic weights n and a fixed time-delay m = 10 ... We set the parameters as η = 10 2. ... C. The scale of social regrets in the case of m = 10 and n = 11 ... We set η = 1/ T for the blue dots and η = 10 2 for the orange ones. ... We set the parameters as η = 10 1 and m = 4. |