Adaptive Active Hypothesis Testing under Limited Information
Authors: Fabio Cecchi, Nidhi Hegde
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now present numerical results based on simulations. In order to gain practical insight, we will focus on a task labelling application. |
| Researcher Affiliation | Collaboration | Fabio Cecchi Eindhoven University of Technology, Eindhoven, The Netherlands f.cecchi@tue.nl Nidhi Hegde Nokia Bell Labs, Paris-Saclay, France nidhi.hegde@nokia-bell-labs.com |
| Pseudocode | No | The paper describes algorithmic rules and policies but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper describes a 'simulated scenario' for its numerical results, implying synthetically generated data rather than a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes running 'simulations' and mentions '1000 runs of the simulation' but does not specify explicit train/validation/test dataset splits. |
| Hardware Specification | No | The paper describes numerical simulations but does not provide any specific hardware details such as GPU models, CPU types, or memory used for running these simulations. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or library versions used for its simulations or analysis. |
| Experiment Setup | Yes | In Figure 1 we present the results of 1000 runs of the simulation for every instance of respectively the first and second scenario described below. Recalling that the simulation stops as soon as maxj νj(t) > 1 δ, we specify that out of the entire set of simulations of these scenarios the algorithm never failed to infer the correct incoming job type j = 1. For both scenarios, in Figure 1(left) we display the averaged sample paths of the coordinate νj (t) and in Figure 1(right) the average sample size required for the decision maker to make an inference. ... Here we present a simulated scenario with J = 100, W = 15, and fixed subsets {Jw}w W satisfying Assumption 2. We set δ 0.001, and assume the incoming job-type to be j = 1. |