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..
Differentiable Submodular Maximization
Authors: Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause
IJCAI 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the effectiveness of our approach for jointly learning and optimizing on synthetic maximum cut data, and on real world applications such as product recommendation and image collection summarization. |
| Researcher Affiliation | Collaboration | Sebastian Tschiatschek1, Aytunc Sahin2 and Andreas Krause2 1 Microsoft Research Cambridge 2 ETH Zurich |
| Pseudocode | Yes | Algorithm 1 PD2GREEDY: Probabilistic diff. double-greedy |
| Open Source Code | No | The paper does not provide an explicit statement or link for the release of its source code. |
| Open Datasets | Yes | We consider the Amazon baby registry data [Gillenwater et al., 2014]. |
| Dataset Splits | Yes | The data is processed using 10-fold cross-validation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments. |
| Software Dependencies | No | The paper mentions software like Adam optimizer and Gurobi but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | For optimization, we used Adam [Kingma and Ba, 2015] with batch size of 16 and initial learning rate 0.02. |