Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds
Authors: Nathanaël Munier, Emmanuel Soubies, Pierre Weiss
JMLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 3 Numerical Experiments 3.1 Measuring Masses in the Solar System 3.2 Blind Deblurring 3.3 Posterior Exploration for an Image Deblurring Problem |
| Researcher Affiliation | Academia | Nathana el Munier EMAIL University of Toulouse, CNRS, IMT, CBI, France Emmanuel Soubies University of Toulouse, CNRS, IRIT, CBI, France Pierre Weiss University of Toulouse, CNRS, IRIT, CBI, France |
| Pseudocode | Yes | Algorithm 1 BFS parameterization of Mε δ(Z) |
| Open Source Code | Yes | The numerical experiments supporting these results, together with the implementation of the Jackpot algorithm, are available in the corresponding Git Hub repository. |
| Open Datasets | Yes | Initial positions and speeds are taken from IMCCE (2019). URL https://ssp.imcce.fr/forms/ephemeris. |
| Dataset Splits | No | The paper discusses data acquisition parameters (e.g., 'measurements are taken on a period of 5 years within intervals of Δt = 7 days'), but does not specify training, validation, or test dataset splits in the context of model training or evaluation. |
| Hardware Specification | No | This work was performed using HPC resources from GENCI-IDRIS (Grant 2021-AD011012210R2). |
| Software Dependencies | No | It relies on the Python package deepinv (Tachella et al., 2025). |
| Experiment Setup | No | The paper mentions general strategies like 'using a gradient descent to high accuracy' and 'using the L-BFGS method', and provides some problem-specific parameters like 'N = 8' for Zernike coefficients and 'ε = 1000 km' for noise standard deviation. However, it lacks specific hyperparameter values for these optimization algorithms such as learning rates, batch sizes, or precise iteration counts. |