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..
Variational Bayesian Optimistic Sampling
Authors: Brendan O'Donoghue, Tor Lattimore
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Figure 2 we show how five agents perform on a 50 50 randomly generated game in self-play and against a best-response opponent. |
| Researcher Affiliation | Industry | Brendan O Donoghue Deep Mind EMAIL Tor Lattimore Deep Mind EMAIL |
| Pseudocode | Yes | Algorithm 1 TS for bandits |
| Open Source Code | No | 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] We included a description of the data generation process for the simulations we ran. |
| Open Datasets | No | The entries of R were sampled from prior N(0, 1) and the noise term ηt at each time-period was also sampled from N(0, 1). |
| Dataset Splits | No | The paper describes data generation processes (e.g., 'entries of R were sampled from prior N(0, 1)') but does not specify explicit train/validation/test dataset splits for reproducibility. |
| Hardware Specification | No | 3. If you ran experiments... (d) Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] In the appendix. (Specific details are not present in this excerpt.) |
| Software Dependencies | No | The paper does not explicitly provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | The entries of R were sampled from prior N(0, 1) and the noise term ηt at each time-period was also sampled from N(0, 1). ... For this experiment we set C = 10... We compare the K-learning and UCB algorithms...over 8 seeds. |