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..
Jackpot: Approximating Uncertainty Domains with Adversarial Manifolds
Authors: Nathanaël Munier, Emmanuel Soubies, Pierre Weiss
JMLR 2025 | Venue PDF | 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. |