Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks
Authors: Rong Zhu, Mattia Rigotti
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We confirm empirically our theory by showing that SAU-based exploration outperforms current state-of-the-art deep Bayesian bandit methods on several real-world datasets at modest computation cost |
| Researcher Affiliation | Collaboration | Rong Zhu Institute of Science and Technology for Brain-inspired Intelligence, Fudan University EMAIL Mattia Rigotti IBM Research AI EMAIL |
| Pseudocode | Yes | Algorithm 1 SAU-UCB and SAU-Sampling for bandit problems |
| Open Source Code | Yes | and make the code to reproduce our results available at https://github.com/ibm/sau-explore. |
| Open Datasets | Yes | Empirical evaluation on real-world Deep Contextual Bandit problems. Table 1 quantifies the performance of SAU-Sampling and SAU-UCB in comparison to the 4 competing baseline algorithms... These results show that a SAU algorithm is the best algorithm in each of the 7 benchmarks... (Mushroom, Statlog, Covertype, Financial, Jester, Adult, Census). |
| Dataset Splits | No | No explicit split percentages or sample counts for training, validation, and test sets are provided in the paper. It refers to benchmarks from a previous paper [18] without detailing the splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory specifications, or cloud instance types) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies or library versions (e.g., Python, PyTorch, TensorFlow, or specific solver versions) are provided in the paper. |
| Experiment Setup | No | No specific experimental setup details such as hyperparameter values (learning rate, batch size, number of epochs) or optimizer settings are explicitly provided in the paper's main text. |