Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks
Authors: Rong Zhu, Mattia Rigotti
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | 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 rongzhu@fudan.edu.cn Mattia Rigotti IBM Research AI mr2666@columbia.edu |
| 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. |