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..
Efficient Pure Exploration in Adaptive Round model
Authors: Tianyuan Jin, Jieming SHI, Xiaokui Xiao, Enhong Chen
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In experiments, our algorithms conduct far fewer rounds, and outperform state of the art by orders of magnitude with respect to query cost. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, University of Science and Technology of China School of Computing, National University of Singapore |
| Pseudocode | Yes | Algorithm 1 Top-k δ-Elimination (k-δE) ... Algorithm 2 Top-k δ-Elimination with Limited Rounds (k-δER) ... Algorithm 3 Uniformly Sampling (US) |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository for the described methodology. |
| Open Datasets | No | The paper describes generating synthetic datasets ('Uniform', 'Normal', 'Segment') for its experiments but does not provide concrete access information (link, DOI, repository, or citation) for them to be publicly available. |
| Dataset Splits | No | The paper describes the datasets used but does not provide specific details on training, validation, or test splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU models, CPU types, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not provide specific version numbers for any software libraries, frameworks, or tools used in the experiments, beyond general references to algorithms or methods. |
| Experiment Setup | Yes | Default parameter values are set as: δ = 0.1, and R = 2. For each setting, the results are averaged over 100 repeated runs. ... We vary ǫ from 0.01 to 0.1, while keeping other parameters unchanged. ... We change 3/4ǫ to 1/2ǫ and set Q to be 8/ǫ2 in our implementation, to gain even better performance. ... We set [17] s parameters following their experimental setting. |