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..
Statistical and Geometrical properties of the Kernel Kullback-Leibler divergence
Authors: Anna Korba, Francis Bach, Clémentine CHAZAL
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we illustrate the validity of our theoretical results and the performance of gradient descent for the regularized KKL. In all our experiments, we consider Gaussian kernels k(x, y) = [...] Our code is available on the github repository https://github.com/clementinechazal/KKL-divergence-gradient-flows.git. |
| Researcher Affiliation | Academia | Clémentine Chazal CREST, ENSAE, IP Paris EMAIL Anna Korba CREST, ENSAE, IP Paris EMAIL Francis Bach INRIA Ecole Normale Supérieure PSL Research university EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available on the github repository https://github.com/clementinechazal/KKL-divergence-gradient-flows.git. |
| Open Datasets | No | The paper uses samples generated from known distributions (e.g., Gaussian, Exponential, uniform on specific shapes) rather than explicitly referring to or providing access information for established publicly available datasets. |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits, nor does it mention a validation set. |
| Hardware Specification | No | The paper states 'Our experiments run on a standard laptop' in the NeurIPS checklist, but does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts. |
| Software Dependencies | No | The paper mentions 'Python code' in relation to its GitHub repository but does not list specific software dependencies with version numbers (e.g., Python version, library versions like PyTorch or NumPy). |
| Experiment Setup | Yes | For each method, we choose a bandwith σ = 0.1, and we optimize the step-size for each method, and sample n = 100 points from the source and target distribution. In Figure 7 and Figure 8, the stepsize h = C d/(d+4) with C = 0.5 and σ is proportional to the mean of distances between particles σ = mean(d(xi, yj) 2 ) 1/2 n 1/(d+4). The bandwidth of k is fixed at σ = 0.1 for Kale and MMD and at σ = 0.3 for KKL. In Figure 13 this time we repeat the experiment but for a simple gradient descent for KKL with constant step h = 0.01. |