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..
Selective Sampling for Online Best-arm Identification
Authors: Romain Camilleri, Zhihan Xiong, Maryam Fazel, Lalit Jain, Kevin G. Jamieson
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present a benchmark experiment validating the fundamental trade-offs that are theoretically characterized in Theorem 1 and Theorem 2. |
| Researcher Affiliation | Academia | Romain Camilleri , Zhihan Xiong , Maryam Fazel, Lalit Jain, Kevin Jamieson University of Washington, Seattle, WA EMAIL |
| Pseudocode | Yes | Algorithm 1 Selective Sampling for Best-arm Identification |
| Open Source Code | No | The paper does not include an unambiguous statement of code release or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes a synthetic data generation process for its experiments but does not use or provide access information for a publicly available or open dataset. |
| Dataset Splits | No | The paper describes an online streaming setting where data is generated IID, rather than using fixed dataset splits (training, validation, test) for reproducibility. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers, needed to replicate the experiment. |
| Experiment Setup | Yes | In this problem, the angle ω is small enough that the item (cos(ω), sin(ω)) is hard to discriminate from the best item e1. ... We swept over the values of τ and plotted on the y-axis the amount of labeled data needed before termination, as shown in Figure 1. ... Our Algorithm uses the solution to (5) for b Pℓ, where we take µb = 2 10 5. |