A Simple Unified Framework for High Dimensional Bandit Problems
Authors: Wenjie Li, Adarsh Barik, Jean Honorio
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide some experiments in order to validate our claims in the theoretical results. We choose to run our algorithms in the different high dimensional bandit problems and validate the corresponding regret upper bounds in Corollary 4.2, Corollary 4.6, Corollary 4.9 and Theorem 5.1. For each problem, we plot the cumulative regret R(T) as well as R(T)/B(T), where B(T) is the upper bound derived in the specific application. For each setting in each problem, we run the algorithm for 10 independent runs and plot the mean results with one standard deviation error bars. |
| Researcher Affiliation | Academia | 1Department of Statistics, Purdue University, West Lafayette, IN 2Department of Computer Science, Purdue University, West Lafayette, IN. |
| Pseudocode | Yes | Algorithm 1 Explore-the-Structure-Then-Commit |
| Open Source Code | No | The paper does not include an unambiguous statement that the authors are releasing their code for the work described in this paper, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | We generate our true θ by randomly choosing its non-zero indices, and then generate each of its non-zero values uniformly randomly from [0,1] and then perform the normalization. We set K = 10 so that there are ten different contexts available at each round. The contexts {xt,ai}K i=1 are generated from the zero-mean and identity-covariance normal distribution. |
| Dataset Splits | No | The paper mentions running algorithms for 10 independent runs and plotting mean results, but does not specify any training, validation, or test dataset splits, nor does it mention cross-validation or specific split percentages/counts for the data used in experiments. |
| Hardware Specification | No | The paper does not explicitly describe the specific hardware used to run its experiments, such as GPU models, CPU models, or cloud computing instances. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | No | The paper provides some setup details like the number of arms (K=10) and data generation methods, but it does not specify concrete hyperparameter values (e.g., learning rate, batch size, number of epochs, optimizer settings) or system-level training configurations. |