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].
Multi-Player Bandits – a Musical Chairs Approach
Authors: Jonathan Rosenski, Ohad Shamir, Liran Szlak
ICML 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present several experiments which validate our theoretical findings. For our experiments, we implemented the DMC algorithm for the dynamic case and the MC algorithm for the static case. For comparison, we implemented the MEGA algorithm of (Avner & Mannor, 2014), which is the current state-of-the-art for our problem setting. |
| Researcher Affiliation | Academia | Jonathan Rosenski EMAIL Weizmann Institute of Science, Rehovot 7610001, Israel Ohad Shamir EMAIL Weizmann Institute of Science, Rehovot 7610001, Israel Liran Szlak EMAIL Weizmann Institute of Science, Rehovot 7610001, Israel |
| Pseudocode | Yes | Algorithm 1 MC, Algorithm 2 Musical Chairs, Algorithm 3 Dynamic MC |
| Open Source Code | No | No explicit statement or link providing access to open-source code for the described methodology was found. |
| Open Datasets | No | No concrete access information (link, DOI, repository, or formal citation) for a publicly available or open dataset was provided. The paper describes generating data based on random distributions: 'The mean rewards of the arms are chosen uniformly at random in [0, 1]'. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) was provided. The paper describes simulation scenarios without explicit train/validation/test splits. |
| Hardware Specification | No | No specific hardware details (exact GPU/CPU models, processor types, or detailed computer specifications) used for running the experiments were provided. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment were provided. |
| Experiment Setup | Yes | For the MC and DMC algorithm, we set T0 to be 3000 in all experiments. For the DMC parameter, T1, we use either the theoretically optimal value presented in this work or that value scaled by a small constant (see details below for the specific value in each experiment). |