PAC Learnability of Node Functions in Networked Dynamical Systems
Authors: Abhijin Adiga, Chris J Kuhlman, Madhav Marathe, S Ravi, Anil Vullikanti
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental results using both synthetic and real-world networks to demonstrate how network structure and sample complexity influence the quality of the inferred system. |
| Researcher Affiliation | Academia | 1Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA. 2Also with the Department of Computer Science, University of Virginia, Charlottesville, VA, USA. 3Also with the Department of Computer Science, University at Albany SUNY, Albany, NY, USA. |
| Pseudocode | Yes | Algorithm 1 A consistent learner for positive examples. |
| Open Source Code | No | The paper does not provide any explicit statement or link for the release of source code for the described methodology. |
| Open Datasets | No | Table 1. Mined and synthetic networks, and their attributes. The paper refers to 'Jazz', 'NRV eu Emall' as mined networks and 'synthetic networks' but does not provide concrete access information (e.g., specific links, DOIs, or formal citations with author names and years) for these datasets. |
| Dataset Splits | No | The paper does not provide specific details regarding training, validation, and test dataset splits, such as percentages, absolute sample counts, or citations to predefined splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. It only generally refers to 'running experiments'. |
| Software Dependencies | No | The paper does not list specific software dependencies with their version numbers (e.g., Python 3.8, PyTorch 1.9, CPLEX 12.4). |
| Experiment Setup | Yes | For each true assignment of thresholds to nodes, Algorithm 1 is used to estimate ten inferred threshold assignments. ... the number nt of configurations C (i.e., comparisons or trials) to be nt = 10n. ... different numbers m of queries (or configurations), ranging from 10 to 105. ... One configuration distribution Du is a uniform distribution where each node is set to state 1 with probability p (and to state 0 with probability (1 p)). ... For Du in the first plot, we take p = 0.25. |