Feasible Arm Identification
Authors: Julian Katz-Samuels, Clay Scott
ICML 2018 | Conference PDF | Archive PDF | Plain Text | 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). |