An Experimental Design Approach for Regret Minimization in Logistic Bandits
Authors: Blake Mason, Kwang-Sung Jun, Lalit Jain7736-7743
AAAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Numerical Evaluation To verify the performance of WAR numerically, we have drawn 20 arms from the a three-dimensional unit sphere. The unknown θ was drawn the same way but scaled to have the norm S {2,4,8}. We have run the naive warmup (5), WAR (2), and the oracle warmup that solves g = minλ X maxx X x 2 Hλ(θ ) 1. We then computed the total number of samples required to satisfy the warmup condition (2) from each method, ignoring the integer effect for simplicity. We repeat this process 5 times and report the result in Table 1 where WAR is significantly better than the naive warmup and not far from the oracle warmup. |
| Researcher Affiliation | Academia | Blake Mason1, Kwang-Sung Jun2, and Lalit Jain3 1Rice University 2University of Arizona 3University of Washington |
| Pseudocode | Yes | Algorithm 1: HOMER: H Optimal MEthod for Regret |
| Open Source Code | No | The paper does not provide an explicit statement or a link to open-source code for the methodology described. |
| Open Datasets | No | The paper describes a synthetic data generation process for its numerical evaluation ('we have drawn 20 arms from the a three-dimensional unit sphere') but does not refer to or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes a numerical evaluation using synthetically generated data and does not specify training, validation, or test dataset splits in terms of percentages or sample counts. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the numerical evaluations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers) used for implementation or experiments. |
| Experiment Setup | Yes | Numerical Evaluation To verify the performance of WAR numerically, we have drawn 20 arms from the a three-dimensional unit sphere. The unknown θ was drawn the same way but scaled to have the norm S {2,4,8}. We have run the naive warmup (5), WAR (2), and the oracle warmup that solves g = minλ X maxx X x 2 Hλ(θ ) 1. We then computed the total number of samples required to satisfy the warmup condition (2) from each method, ignoring the integer effect for simplicity. We repeat this process 5 times and report the result in Table 1 where WAR is significantly better than the naive warmup and not far from the oracle warmup. |