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..
GLAD: Learning Sparse Graph Recovery
Authors: Harsh Shrivastava, Xinshi Chen, Binghong Chen, Guanghui Lan, Srinivas Aluru, Han Liu, Le Song
ICLR 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that the AM architecture provides very good inductive bias, allowing the model to learn very effective sparse graph recovery algorithm with a small amount of training data.In this section, we report several experiments to compare GLAD with traditional algorithms and other data-driven algorithms. |
| Researcher Affiliation | Collaboration | {1School of Computational Science & Engineering, 2School of Mathematics, 3School of Industrial and Systems Engineering }at Georgia Institute of Technology, 4Computer Science Department at Northwestern University, 5Ant Financial Services Group |
| Pseudocode | Yes | Algorithm 1: GLAD and Algorithm 2: ADMMu |
| Open Source Code | Yes | Python implementation (tested on P100 GPU) is available1. 1code: https://drive.google.com/open?id=16POE4TMp7UUie_Lc_Lq_Rz_STqzk_VHm2stl_M |
| Open Datasets | Yes | For sections 5.1 and 5.2, the synthetic data was generated based on the procedure described in Rolfs et al. (2012).The Syn TRe N (Van den Bulcke et al., 2006) is a synthetic gene expression data generator specifically designed for analyzing the structure learning algorithms.We use the real data from the DREAM 5 Network Inference challenge (Marbach et al., 2012). |
| Dataset Splits | Yes | For finetuning the traditional algorithms, a validation dataset of 10 graphs was used. For the GLAD algorithm, 10 training graphs were randomly chosen and the same validation set was used. |
| Hardware Specification | Yes | Python implementation (tested on P100 GPU) is available1.Convergence results and average runtime of different algorithms on Nvidia s P100 GPUs are shown in Figure 4 and Table 2 respectively. |
| Software Dependencies | No | The paper mentions 'Python implementation' and 'sklearn package Pedregosa et al. (2011)' but does not provide specific version numbers for Python, scikit-learn, or any other software libraries. |
| Experiment Setup | Yes | GLAD parameter settings: ρnn was a 4 layer neural network and Λnn was a 2 layer neural network. Both used 3 hidden units in each layer. The non-linearity used for hidden layers was tanh, while the final layer had sigmoid (σ) as the non-linearity for both, ρnn and Λnn (refer Figure 3). The learnable offset parameter of initial Θ0 was set to t = 1. It was unrolled for L = 30 iterations. The learning rates were chosen to be around [0.01, 0.1] and multi-step LR scheduler was used. The optimizer used was adam. |