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..
Optimal Parallelization of Boosting
Authors: Arthur da Cunha, Mikael Møller Høgsgaard, Kasper Green Larsen
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we essentially close this gap by providing both improved lower bounds on the parallel complexity of weak-to-strong learners, and a parallel Boosting algorithm whose performance matches these bounds across the entire p vs. t compromise spectrum, up to logarithmic factors. Ultimately, this work settles the parallel complexity of Boosting algorithms that are nearly sample-optimal. |
| Researcher Affiliation | Academia | Arthur da Cunha Department of Computer Science Aarhus University EMAIL Mikael Møller Høgsgaard Department of Computer Science Aarhus University EMAIL Kasper Green Larsen Department of Computer Science Aarhus University EMAIL |
| Pseudocode | Yes | Algorithm 1: Proposed parallel Boosting algorithm |
| Open Source Code | No | The paper is theoretical, and we have no experiments, data or code in the paper. |
| Open Datasets | No | The paper is theoretical, and we have no experiments, data or code in the paper. |
| Dataset Splits | No | The paper is theoretical, and we have no experiments, data or code in the paper. |
| Hardware Specification | No | The paper is theoretical, and we have no experiments, data or code in the paper. |
| Software Dependencies | No | The paper is theoretical, and we have no experiments, data or code in the paper. |
| Experiment Setup | No | The paper is theoretical, and we have no experiments, data or code in the paper. |