Deep Hierarchy in Bandits
Authors: Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We confirm these theoretical findings empirically, in both synthetic and real-world experiments. |
| Researcher Affiliation | Collaboration | 1University of California, Berkeley 2Amazon 3Deep Mind 4Google Research. |
| Pseudocode | Yes | Algorithm 1 Hier TS: Hierarchical Thompson sampling. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | We use the CIFAR-100 dataset (Krizhevsky, 2009), with 60 000 images of size 32 32. |
| Dataset Splits | No | The paper specifies "50 000 training and 10 000 test images" for CIFAR-100 but does not explicitly mention a validation dataset split. |
| Hardware Specification | No | The paper does not specify any hardware details such as exact GPU or CPU models used for running the experiments. |
| Software Dependencies | No | The paper mentions using an "Efficient Net-L2 network" but does not provide specific version numbers for any software components, libraries, or programming languages used in the experiments. |
| Experiment Setup | Yes | All algorithms are run for n = 500 rounds and evaluated by the Bayes regret on 100 independent samples of Θ . |