Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Deep Hierarchy in Bandits
Authors: Joey Hong, Branislav Kveton, Sumeet Katariya, Manzil Zaheer, Mohammad Ghavamzadeh
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 27,046 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,004 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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 . |