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..
Parallel Submodular Function Minimization
Authors: Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We consider the parallel complexity of submodular function minimization (SFM). We provide a pair of methods which obtain two new query versus depth tradeoffs a submodular function defined on subsets of n elements that has integer values between M and M. The first method has depth 2 and query complexity n O(M) and the second method has depth e O(n1/3M 2/3) and query complexity O(poly(n, M)). |
| Researcher Affiliation | Collaboration | Deeparnab Chakrabarty Dartmouth College Hanover, USA EMAIL Andrei Graur Stanford University Stanford, USA EMAIL Haotian Jiang Microsoft Research Redmond, USA EMAIL Aaron Sidford Stanford University Stanford, USA EMAIL |
| Pseudocode | Yes | Algorithm 1: Augmenting Sets Algorithm |
| Open Source Code | No | The paper is theoretical and focuses on algorithm design and complexity analysis. It does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus no information about publicly available or open datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments on datasets, thus no information about training/validation/test dataset splits is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental setups or hardware used for computation. |
| Software Dependencies | No | The paper is theoretical and does not describe experimental implementations or software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not conduct experiments, therefore no experimental setup details like hyperparameters or training configurations are provided. |