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..
Stochastic Probing with Increasing Precision
Authors: Martin Hoefer, Kevin Schewior, Daniel Schmand
IJCAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study both identical and non-identical distributions and develop polynomial-time algorithms with constant approximation factors in both scenarios. Our main results are two probing algorithms, one for identically distributed items and one for non-identical distributions. We show that both run in polynomial time and obtain a constant-factor approximation of the optimal probing strategy that maximizes the expected value of the selected item. |
| Researcher Affiliation | Academia | 1Goethe University Frankfurt, Germany 2University of Cologne, Germany 3University of Bremen, Germany |
| Pseudocode | Yes | Algorithm 1: ALGgen for General Distributions |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on algorithm design and approximation factors; it does not use or reference any datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental validation on datasets with specific splits. |
| Hardware Specification | No | The paper does not specify any hardware used for experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers, as it is a theoretical work. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or system-level training settings. |