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..
Batched Coarse Ranking in Multi-Armed Bandits
Authors: Nikolai Karpov, Qin Zhang
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce and study the problem of batched coarse ranking in multi-armed bandits. We propose two novel algorithms based on the principle of optimism in the face of uncertainty and Thompson Sampling, and provide theoretical guarantees for their worst-case query complexity. We present extensive numerical results demonstrating improved performance over existing methods on both synthetic and real-world datasets. |
| Researcher Affiliation | Collaboration | Andrew L. Liu (University of Toronto, Carnegie Mellon University), Zhiyao Lei (University of Toronto), Gauri Joshi (Carnegie Mellon University), Venkatesan Guruswami (Amazon Web Services, Carnegie Mellon University) |
| Pseudocode | Yes | Algorithm 1: BATCH-C-UCB (on page 5) Algorithm 2: BATCH-C-TS (on page 6) |
| Open Source Code | No | The paper does not provide any statements or links regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | MovieLens 1M dataset [10] |
| Dataset Splits | Yes | We randomly partition the data into a training set (80%) and a testing set (20%). |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU models, CPU types, or cloud computing resources) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, or specific solvers). |
| Experiment Setup | Yes | For BATCH-C-UCB and BATCH-C-TS, we choose B = 10 batches and N = 200 total samples. For the sequential algorithms, we set batch size b = 1. We consider the top 20 arms (movies) as the elite set. We set the reward threshold ยต0 = 3.5, the batch size for BATCH-C-UCB and BATCH-C-TS as B = 10, the total number of samples N = 200, and the confidence parameter ฮด = 0.05. |