Robust Pure Exploration in Linear Bandits with Limited Budget
Authors: Ayya Alieva, Ashok Cutkosky, Abhimanyu Das
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We complement our theoretical results with empirical validation. In this section, we empirically validate our approach via synthetic experiments. |
| Researcher Affiliation | Collaboration | 1Stanford University, Stanford, California, USA 2Boston University, Boston, Massachussetts, USA 3Google Research, Mountain View, California, USA. |
| Pseudocode | Yes | We provide the pseudocode for our pure exploration algorithm in Algorithms 1 and 2. |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of source code for the described methodology or a link to a code repository. |
| Open Datasets | No | The paper describes synthetic experiments using self-generated data distributions (Uniform, Gaussian Mixture, Clusters, Subspace) but does not refer to a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper does not specify dataset splits (e.g., train/validation/test percentages or counts) needed to reproduce data partitioning. It mentions using synthetic data averaged over trials, but not fixed splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory, or cloud instance types). |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers, to reproduce the experiments. |
| Experiment Setup | Yes | The experiments used d = 3, n = 50, T = 800, and are averaged over 200 trials. For instance, while the real σ and real η are both equal to 1.0 in our setting, the grey area was sampled from 0.5 to 3.0 for σ, and from 0.5 to 2.0 for η. |