Differentiable Submodular Maximization
Authors: Sebastian Tschiatschek, Aytunc Sahin, Andreas Krause
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | 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. |