Transfer Learning for Latent Variable Network Models
Authors: Akhil Jalan, Arya Mazumdar, Soumendu Sundar Mukherjee, Purnamrita Sarkar
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically demonstrate our algorithm s use on real-world and simulated network estimation problems. |
| Researcher Affiliation | Academia | Akhil Jalan Department of Computer Science UT Austin akhiljalan@utexas.edu Arya Mazumdar Halıcıo glu Data Science Institute & Dept of CSE UC San Diego arya@ucsd.edu Soumendu Sundar Mukherjee Statistics and Mathematics Unit (SMU) Indian Statistical Institute, Kolkata ssmukherjee@isical.ac.in Purnamrita Sarkar Department of Statistics and Data Sciences UT Austin purna.sarkar@austin.utexas.edu |
| Pseudocode | Yes | Algorithm 1 b Q-Estimation for Latent Variable Models |
| Open Source Code | Yes | We submit our code as a supplementary zip file in accordance with the Neur IPS code and data submission guidelines. |
| Open Datasets | Yes | Metabolic Networks. We access metabolic models from King et al. (2016) at http://bigg.ucsd.edu. (...) EMAIL-EU. We use the email-EU-core-temporal dataset at https://snap.stanford.edu/data/email-Eu-core-temporal.html, as introduced in Paranjape et al. (2017). |
| Dataset Splits | No | The paper describes using source and target data for estimation, but does not explicitly mention or specify training, validation, and test dataset splits or cross-validation procedures for model evaluation. |
| Hardware Specification | No | As described in Appendix C, 'We run all experiments on a personal Linux machine with 378GB of CPU/RAM.' This description does not include specific CPU or GPU models, or other detailed hardware specifications. |
| Software Dependencies | No | The paper does not explicitly list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific libraries). |
| Experiment Setup | Yes | Hyperparameters. We do not tune any hyperparameters. For Algorithm 1 we use the quantile cutoff hn = qn Q in all experiments. |