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; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1]
On Regularized Radon-Nikodym Differentiation
Authors: Duc Hoan Nguyen, Werner Zellinger, Sergei Pereverzyev
JMLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results are illustrated by numerical simulations. Finally, we present some numerical illustrations supporting our theoretical results. |
| Researcher Affiliation | Academia | Duc Hoan Nguyen EMAIL Johann Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Altenberger Straße 69, 4040 Linz, Austria University of Lorraine, CNRS, CRAN, Nancy, F-54000, France Werner Zellinger EMAIL Sergei Pereverzyev EMAIL Johann Radon Institute for Computational and Applied Mathematics Austrian Academy of Sciences Altenberger Straße 69, 4040 Linz, Austria |
| Pseudocode | Yes | βλ,l X,0 = 0, βλ,l X = (nλI + K) 1 nλβλ,l 1 X + F , l = 1, 2, . . . , k. |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository for the methodology described. |
| Open Datasets | No | In our examples, we simulate inputs Xp = (x1, x2, . . . , xn) to be sampled from the normal distribution p N(2, 5), while the inputs Xq = (x 1, x 2, . . . , x m) are sampled from the normal distribution q N(µq, 0.5) with µq = {2, 3, 4}. |
| Dataset Splits | No | The paper describes generating synthetic data by sampling from normal distributions and specifies sample sizes as m = n = 100. It does not refer to splitting a pre-existing dataset into training, validation, or test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details, such as library names with version numbers. |
| Experiment Setup | Yes | The algorithm Eq. 35 has been implemented with m = n = 100 and k = {1, 2, 3, 5, 10}. The regularization parameter λ is chosen by the so-called quasi-optimality criterion... we choose λ0 = 0.9, ϱ = 9q 1 9, and w = 9, such that λι [0.1, 0.9]. |