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..
Agnostic Continuous-Time Online Learning
Authors: Pramith Devulapalli, Changlong Wu, Ananth Grama, Wojciech Szpankowski
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Answer: [NA] Justification: This is a pure theory paper with no experiments. |
| Researcher Affiliation | Academia | Pramith Devulapalli ,1, Changlong Wu ,1,2,3, Ananth Grama1,2 & Wojciech Szpankowski1,2,4 1Department of Computer Science, Purdue University 2Center for Science of Information and Institute for Physical Artificial Intelligence, Purdue Univ. 3 University of Arizona 4Jagiellonian University EMAIL EMAIL EMAIL |
| Pseudocode | Yes | Algorithm 1 Uniform Random Query with Fixed Epochs Algorithm 2 Uniform Random Query with Dynamic Epochs Algorithm 3 Weighted-EWA |
| Open Source Code | No | Answer: [NA] Justification: This is a pure theory paper. |
| Open Datasets | No | Answer: [NA] Justification: This is a pure theory paper. |
| Dataset Splits | No | Answer: [NA] Justification: This is a pure theory paper. |
| Hardware Specification | No | Answer: [NA] Justification: This is a pure theory paper. |
| Software Dependencies | No | Answer: [NA] Justification: This is a pure theory paper. |
| Experiment Setup | No | Answer: [NA] Justification: This is a pure theory paper. |