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..
Multivariate Submodular Optimization
Authors: Richard Santiago, F. Bruce Shepherd
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our focus is on a more general class of multivariate submodular optimization (MVSO) problems: min / max f(S1, S2, . . . , Sk) : S1 S2 Sk F. For maximization, we show that practical algorithms such as accelerated greedy variants and distributed algorithms achieve good approximation guarantees for very general families (such as matroids and p-systems). For arbitrary families, we show that monotone (resp. nonmonotone) MVSO admits an α(1 1/e) (resp. α 0.385) approximation whenever monotone (resp. nonmonotone) SO admits an α-approximation over the multilinear formulation. This substantially expands the family of tractable models. On the minimization side we give essentially optimal approximations in terms of the curvature of f. |
| Researcher Affiliation | Academia | 1School of Computer Science, Mc Gill University, Montreal, Canada 2Department of Computer Science, University of British Columbia, Vancouver, Canada. Correspondence to: Richard Santiago <EMAIL>. |
| Pseudocode | No | The paper is theoretical and describes algorithms conceptually but does not include structured pseudocode blocks or figures labeled "Algorithm". |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments involving datasets, training, or public dataset access. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments, therefore no dataset split information (training, validation, test) is provided. |
| Hardware Specification | No | The paper is theoretical and does not report on experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on experiments or provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup, hyperparameters, or training configurations. |