Reproducibility Index

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..

Tighter Expected Generalization Error Bounds via Wasserstein Distance

Authors: Borja Rodríguez Gálvez, German Bassi, Ragnar Thobaben, Mikael Skoglund

NeurIPS 2021 | Venue PDF | LLM Run Details

Reproducibility Variable	Result	LLM Response
Research Type	Theoretical	Example 1 (Gaussian location model). Consider the problem of estimating the mean µ of a d-dimensional Gaussian distribution with known covariance matrix σ2Id. Further consider that there are n samples S = (Z1, . . . , Zn) available, the loss is measured with the Euclidean distance ℓ(w, z) = w z 2, and the estimation is their empirical mean W = 1 n Pn i=1 Zi. In this example, the expected generalization error can be calculated exactly (see Appendix E): gen(W, S) = dσ2/(2n). ... Figure 1: Expected generalization error and generalization error bounds for the Gaussian location model with N(µ, 1) (left) and N(µ, I250) (right). See Appendix E for the details.
Researcher Affiliation	Collaboration	Borja Rodríguez-Gálvez KTH Royal Institute of Technology Stockholm, Sweden EMAIL; Germán Bassi Ericsson Research Stockholm, Sweden EMAIL; Ragnar Thobaben KTH Royal Institute of Technology Stockholm, Sweden EMAIL; Mikael Skoglund KTH Royal Institute of Technology Stockholm, Sweden EMAIL
Pseudocode	No	The paper describes mathematical proofs and outlines their steps but does not include any pseudocode or algorithm blocks with structured steps.
Open Source Code	No	The paper's checklist under “3. If you ran experiments...” explicitly states “[N/A]” for “Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)?”. No other statement about code release is found.
Open Datasets	No	The paper uses a theoretical “Gaussian location model” as an example for analytical calculations, which defines samples (Z_i) from a distribution (P_Z). It does not use or provide concrete access information (link, citation, repository) for a publicly available, named dataset.
Dataset Splits	No	The paper is theoretical and presents analytical calculations for a specific model; it does not involve empirical experiments with data splits for training, validation, or testing.
Hardware Specification	No	The paper is theoretical and involves analytical derivations and calculations rather than empirical experiments, thus no hardware specifications for running experiments are mentioned.
Software Dependencies	No	The paper is theoretical and does not describe software used for its analysis, thus no software dependencies with version numbers are provided.
Experiment Setup	No	The paper is theoretical and presents analytical derivations and comparisons. It does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings, as no empirical experiments were conducted.