Efficient PAC Learnability of Dynamical Systems Over Multilayer Networks
Authors: Zirou Qiu, Abhijin Adiga, Madhav Marathe, S. S. Ravi, Daniel Rosenkrantz, Richard Stearns, Anil Kumar Vullikanti
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present experimental studies on the relationships between model parameters and the empirical performance of our PAC algorithm. Here, we study the performance of the algorithm on a variety of different networks (Magnani et al., 2013; Omodei et al., 2015; Stark et al., 2006; Coleman et al., 1957), as shown in Table 1. |
| Researcher Affiliation | Academia | 1University of Virginia, Charlottesville, VA, USA 2Biocomplexity Institute and Initiative, University of Virginia, Charlottesville, VA, USA 3Department of Computer Science, University at Albany SUNY, Albany, NY, USA. |
| Pseudocode | No | The algorithm is described in natural language within Section 3.1 'An Efficient PAC Learner', but no formal pseudocode block, algorithm box, or structured code-like steps are provided. |
| Open Source Code | Yes | Our source code (in C++ and Python), documentation, and selected datasets are available at https://github.com/bridgelessqiu/ Learning-Multilayer-Dynamical-Systems-ICML24. |
| Open Datasets | Yes | We study the performance of the algorithm on a variety of different networks (Magnani et al., 2013; Omodei et al., 2015; Stark et al., 2006; Coleman et al., 1957), as shown in Table 1. |
| Dataset Splits | No | The paper mentions training sets and evaluating on sampled configurations from a distribution D, but it does not specify explicit training/validation/test dataset splits (e.g., percentages or counts) or refer to standard pre-defined splits for reproducibility. |
| Hardware Specification | Yes | All experiments were performed on Intel Xeon(R) Linux machines with 64GB of RAM. |
| Software Dependencies | No | The paper mentions that the source code is in 'C++ and Python' but does not list specific software dependencies with version numbers (e.g., library versions, compiler versions, or specific solver versions). |
| Experiment Setup | Yes | For each network, we have a target system h where the threshold of each vertex v V on each layer i is in [0, degi(v) + 2]. For each such h , a training set T = {(Ci, h (Ci))}q i=1 is constructed, where each Ci is sampled from a distribution D. We consider distributions where the state of each vertex in Ci T is 0 w.p. p and 1 w.p. 1 p, for a p {0.1, 0.5, 0.9}. [...] Next, we study the relationship between ℓand σ, under a fixed |T | = 500 over different distributions. [...] Lastly, we study the effect of k on the loss ℓusing multilayer Gnp networks of size 500 and average degree (on each layer) of 10. The number of layers is increased from 2 to 6 while |T | is fixed at 500. The result is shown in Fig 3(b) for three values of σ. |