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..
Interactive Algorithms: Pool, Stream and Precognitive Stream
Authors: Sivan Sabato, Tom Hess
JMLR 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide algorithms and matching lower bounds for general pool algorithms, and for utility-based pool algorithms. We further derive nearly matching upper and lower bounds on the gap between the two settings for the special case of active learning for binary classification. |
| Researcher Affiliation | Academia | Sivan Sabato EMAIL Tom Hess EMAIL Department of Computer Science Ben-Gurion University of the Negev Beer Sheva 8410501, Israel. |
| Pseudocode | Yes | Algorithm 1 Algorithm Await Algorithm 2 Algorithm Anowait Algorithm 3 Algorithm Agen Algorithm 4 AU p Algorithm 5 Sec Pr Var Algorithm 6 AU s |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It only provides licensing information for the paper itself. |
| Open Datasets | No | The paper discusses abstract elements and responses drawn from theoretical distributions, such as 'Let D be a distribution over X Y'. It does not refer to any specific publicly available or open datasets for empirical evaluation. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not conduct experiments using specific datasets, therefore, no dataset split information is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setups requiring specific hardware, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and presents algorithms in pseudocode. It does not describe any implementation details that would require listing specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical, presenting algorithms and mathematical proofs. It does not include an experimental section with specific setup details, hyperparameters, or training configurations. |