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 Convergence of Regularized Learning in Games
Authors: Vasilis Syrgkanis, Alekh Agarwal, Haipeng Luo, Robert E. Schapire
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we simulate a 4-bidder simultaneous auction game, and compare our optimistic algorithms against Hedge [7] in terms of utilities, regrets and convergence to equilibria. We run the game for n = 4 bidders and m = 4 items and valuation v = 20. The bids are discretized to be any integer in [1, 20]. We ο¬nd that the sum of the regrets and the maximum individual regret of each player are remarkably lower under Optimistic Hedge as opposed to Hedge. In Figure 1 we plot the maximum individual regret as well as the sum of the regrets under the two algorithms, using = 0.1 for both methods. |
| Researcher Affiliation | Collaboration | Vasilis Syrgkanis Microsoft Research New York, NY EMAIL Alekh Agarwal Microsoft Research New York, NY EMAIL Haipeng Luo Princeton University Princeton, NJ EMAIL Robert E. Schapire Microsoft Research New York, NY EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. |
| Open Datasets | No | The paper describes a simulated auction game without providing concrete access information (link, DOI, repository, formal citation) for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes a simulation of game dynamics for 10,000 rounds but does not provide specific dataset split information (percentages, sample counts, or detailed splitting methodology) for reproducibility. |
| Hardware Specification | No | The paper mentions simulating a game but does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions algorithms like 'Optimistic Hedge' and 'Hedge' but does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We run the game for n = 4 bidders and m = 4 items and valuation v = 20. The bids are discretized to be any integer in [1, 20]. We ο¬nd that the sum of the regrets and the maximum individual regret of each player are remarkably lower under Optimistic Hedge as opposed to Hedge. In Figure 1 we plot the maximum individual regret as well as the sum of the regrets under the two algorithms, using = 0.1 for both methods. |