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..
Optimization for Approximate Submodularity
Authors: Yaron Singer, Avinatan Hassidim
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we describe a technique that yields strong guarantees for maximization of monotone submodular functions from approximate surrogates under cardinality and intersection of matroid constraints. In particular, we show tight guarantees for maximization under a cardinality constraint and 1/(1 + P) approximation under intersection of P matroids. |
| Researcher Affiliation | Collaboration | Avinatan Hassidim Bar Ilan University and Google EMAIL Yaron Singer Harvard University EMAIL |
| Pseudocode | Yes | Algorithm 1 SM-GREEDY; Algorithm 2 SM-MATROID-GREEDY |
| Open Source Code | No | The paper does not provide concrete access to source code. |
| Open Datasets | No | This is a theoretical paper that does not use or reference any datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not describe data splitting for training, validation, or testing. |
| Hardware Specification | No | This is a theoretical paper and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | This is a theoretical paper and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | This is a theoretical paper and does not describe an experimental setup with hyperparameters or training configurations. |