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 [1].
Non-Stationary Delayed Bandits with Intermediate Observations
Authors: Claire Vernade, Andras Gyorgy, Timothy Mann
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results demonstrate that our method is able to learn in non-stationary delayed environments where existing methods fail. |
| Researcher Affiliation | Industry | 1DeepMind, London, UK. |
| Pseudocode | Yes | Algorithm 1 NSD-UCRL2 for NSD-Bandits |
| Open Source Code | No | The paper does not provide concrete access to source code (specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described in this paper. |
| Open Datasets | No | The experiments use a synthetic setup based on parameters in Table 1, not a publicly available dataset with concrete access information (link, DOI, repository, or formal citation). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning. It mentions '50 independent runs' but no train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment. |
| Experiment Setup | Yes | Throughout the experiments we use the transition probabilities and mean rewards as reported in Table 1. The delay parameter D and the window size W of the algorithm are discussed in the dedicated sections. ... we run experiments with time horizon T = 8000 with ΓT = 3 change points at rounds {2000, 4000, 6000}. In the next experiments, we will set W = 800, which is close to the value suggested by theory and that works best in this calibration experiment. |