Efficient Pure Exploration in Adaptive Round model
Authors: Tianyuan Jin, Jieming SHI, Xiaokui Xiao, Enhong Chen
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | 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. |