Laplace Transform Based Low-Complexity Learning of Continuous Markov Semigroups
Authors: Vladimir R Kostic, Karim Lounici, Hélène Halconruy, Timothée Devergne, Pietro Novelli, Massimiliano Pontil
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate our theoretical findings in several experiments. Our experiments show a striking performance improvement over TO-based and other IG methods, as predicted by our theory. |
| Researcher Affiliation | Academia | 1CSML, Istituto Italiano di Tecnologia, Genova, Italy 2Faculty of Science, University of Novi Sad, Serbia 3CMAP, Ecole Polytechnique, Paris, France 4SAMOVAR, T el ecom Sud-Paris, Institut Polytechnique de Paris, France 5MODAL X, Universit e Paris Nanterre, France 6AI Center, University College London, UK. |
| Pseudocode | Yes | Algorithm 1 Primal La RRR Algorithm 2 Dual La RRR |
| Open Source Code | Yes | The implementation of the methods, as well as all experiments are available in the Git Hub repository. |
| Open Datasets | Yes | We use two independent trajectories from (N uske et al., 2017) to train and validate the model, respectively. |
| Dataset Splits | Yes | We use two independent trajectories from (N uske et al., 2017) to train and validate the model, respectively. |
| Hardware Specification | No | No specific hardware details (like GPU/CPU models, processor types, or memory amounts) are mentioned in the paper. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library or solver names with version numbers) used to replicate the experiment. |
| Experiment Setup | Yes | As features we use 1000 random Fourier features, and test if we can recover eigenvalues of the non-normal linear drift. In Fig. 1 we compare to TO RRR estimator over ten trials and for two different time-discretizations. Number of samples for t = 1e 2 is n = 1e4, while for t = 1e 3 is n = 1e5. |