Stochastic Probing with Increasing Precision
Authors: Martin Hoefer, Kevin Schewior, Daniel Schmand
IJCAI 2021 | Conference PDF | Archive PDF | Plain Text | 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. |