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].
Efficient Minimax Strategies for Square Loss Games
Authors: Wouter M. Koolen, Alan Malek, Peter L Bartlett
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We derive the minimax solutions for the case where the prediction and action spaces are the simplex (this setup is sometimes called the Brier game) and the ℓ2 ball (this setup is related to Gaussian density estimation). We show that in both cases the value of each sub-game is a quadratic function of a simple statistic of the state, with coefficients that can be efficiently computed using an explicit recurrence relation. The resulting deterministic minimax strategy and randomized maximin strategy are linear functions of the statistic. |
| Researcher Affiliation | Academia | Wouter M. Koolen Queensland University of Technology and UC Berkeley EMAIL Alan Malek University of California, Berkeley EMAIL Peter L. Bartlett University of California, Berkeley and Queensland University of Technology EMAIL |
| Pseudocode | No | The paper contains mathematical derivations and proofs but no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not use datasets, so there is no mention of training/validation/test splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct experiments, therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not conduct experiments, therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not include an experimental setup with hyperparameters or training settings. |