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..
Feasible Arm Identification
Authors: Julian Katz-Samuels, Clay Scott
ICML 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the effectiveness of our algorithms on synthetic and real-world datasets. |
| Researcher Affiliation | Academia | 1 Department of Computer Science and Electrical Engineering, University of Michigan. |
| Pseudocode | Yes | Algorithm 1 MD-UCBE: Multi-dimensional Upper Confidence Bound Exploration algorithm; Algorithm 2 MD-SAR: Multi-dimensional Successive Accepts and Rejects algorithm; Algorithm 3 MD-APT: Multi-dimensional Anytime Parameter-Free Thresholding algorithm. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | We investigate this problem by considering the data in Genovese et al. (2013) (see ARCR20 in week 16 in Table 2 and Table 3). [...] We use a real-world dataset for the natural language processing task of affective text analysis (Snow et al., 2008). |
| Dataset Splits | No | The paper describes a multi-armed bandit problem with a fixed budget, not a typical supervised learning setup with explicit training, validation, and test splits for data. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | To calculate p pϵq i,t , we use the quadratic programming solver in the CVXOPT package for python. While the software name is mentioned, specific version numbers for CVXOPT or Python are not provided. |
| Experiment Setup | Yes | Each experiment has 20 5-dimensional arms and is run for 2000 time steps. We use Gaussian distributions with variance 1/4. For experiments 1, 2, and 3 we use a cube P tx P R5 : 0 ď xi ď 1u. [...] We run the experiment for 1000 time steps (Dose-Finding). [...] We run each algorithm for 4000 time steps (Crowdsourcing). |