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..
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 | Venue PDF | 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 โand ฯ, under a fixed |T | = 500 over different distributions. [...] Lastly, we study the effect of k on the loss โusing 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 ฯ. |