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].
Linear Stochastic Bandits Under Safety Constraints
Authors: Sanae Amani, Mahnoosh Alizadeh, Christos Thrampoulidis
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our simulation results in Appendix F emphasize the critical role of a sufficiently long pure exploration phase by Safe-LUCB as suggested by Lemma 2. Specifically, Figure 1b depicts an instance where no exploration leads to significantly worse order of regret. |
| Researcher Affiliation | Academia | Sanae Amani University of California, Santa Barbara EMAIL Mahnoosh Alizadeh University of California, Santa Barbara EMAIL Christos Thrampoulidis University of California, Santa Barbara EMAIL |
| Pseudocode | Yes | Algorithm 1 Safe-LUCB |
| Open Source Code | No | The paper does not contain any explicit statements about providing open-source code or links to a code repository for the described methodology. |
| Open Datasets | No | The paper conducts simulations using a 'simplified setting of K-armed linear bandits' but does not specify or provide access information for any publicly available or open dataset. |
| Dataset Splits | No | The paper discusses simulation results but does not provide specific details on dataset splits (e.g., train/validation/test percentages or sample counts) for reproducibility. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., CPU, GPU models, or memory) used for running its experiments or simulations. |
| Software Dependencies | No | The paper does not list any specific software dependencies, libraries, or their version numbers used in the implementation or simulations. |
| Experiment Setup | No | The paper mentions 'The details on the parameters of the simulations are deferred to Appendix F' but does not provide specific experimental setup details (e.g., hyperparameter values, training configurations) within the main text. |