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..
Nonparametric estimation of continuous DPPs with kernel methods
Authors: Michaël Fanuel, Rémi Bardenet
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Empirical evaluation |
| Researcher Affiliation | Academia | Michaël Fanuel and Rémi Bardenet Université de Lille, CNRS, Centrale Lille UMR 9189 CRISt AL, F-59000 Lille, France EMAIL |
| Pseudocode | Yes | Algorithm 1 Estimation of the integral kernel a(x, y) of the DPP likelihood kernel A. and Algorithm 2 Estimation of the integral kernel k(x, y) of the DPP correlation kernel K = A(A + I) 1. |
| Open Source Code | Yes | Our code is freely available1. 1https://github.com/mrfanuel/Learning Continuous DPPs.jl |
| Open Datasets | No | We draw samples3 from this continuous DPP in the window X = [0, 1]2. This is simulated data, not a pre-existing publicly available dataset. |
| Dataset Splits | No | The paper describes generating 's i.i.d. samples of a DPP' and 'sample a set of points I = {x i : 1 i n} i.i.d. from the ambient probability measure µ' but does not specify traditional train/validation/test dataset splits. |
| Hardware Specification | No | The paper mentions that compute details are in supplementary material, but the provided text does not contain specific hardware details like GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions using 'the R package spatstat [Baddeley et al., 2015]' but does not provide specific version numbers for software dependencies used in their implementation. |
| Experiment Setup | Yes | We consider an L-ensemble with correlation kernel k(x, y) = ρ exp( x y 2 2/α2) defined on Rd with α = 0.05. For the estimation, we use a Gaussian kernel k H(x, y) = exp x y 2 2/(2σ2) with σ > 0. The computation of the correlation kernel always uses p = 1000 uniform samples. Iteration (14) is run until the precision threshold tol = 10 5 is achieved. For stability, we add 10 10 to the diagonal of the Gram matrix K. The remaining parameter values are given in captions. (e.g., σ = 0.1 and λ = 0.1, ρ = 50 and ρ = 100) |