Non-parametric Models for Non-negative Functions
Authors: Ulysse Marteau-Ferey, Francis Bach, Alessandro Rudi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The paper is complemented by an experimental evaluation of the model showing its effectiveness in terms of formulation, algorithmic derivation and practical results on the problems of density estimation, regression with heteroscedastic errors, and multiple quantile regression. |
| Researcher Affiliation | Academia | Ulysse Marteau-Ferey Francis Bach Alessandro Rudi INRIA École Normale Supérieure PSL Reasearch University |
| Pseudocode | No | The paper discusses algorithmic approaches (e.g., FISTA) but does not include a structured pseudocode block or a clearly labeled algorithm figure. |
| Open Source Code | Yes | The code for these experiments is available on Git Hub (https://github.com/umarteau/non_negative_model). |
| Open Datasets | No | The paper describes generating synthetic data for its experiments ('n = 50 i.i.d. points sampled from (x) = 1/2N(-1, 0.3) + 1/2N(1, 0.3)', 'data are sampled', 'a given conditional distribution P(Y|x)') but does not provide access information (link, DOI, formal citation) for a publicly available dataset. |
| Dataset Splits | No | The paper states 'Full cross-validation has been applied to each model independently, to find the best λ, λ1, λ2' but does not specify the type (e.g., k-fold) or the exact split percentages or sample counts for reproducibility. |
| Hardware Specification | No | The paper does not provide specific details regarding the hardware (e.g., GPU, CPU models, or cloud computing instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using FISTA for optimization but does not provide specific software dependencies (e.g., programming languages, libraries, or solvers) with version numbers. |
| Experiment Setup | Yes | For all methods we used (A) = λ1k Ak + λ2 F or (w) = λ kwk2. We used the Gaussian kernel k(x, x0) = exp( kx x0k2/(2σ2)) with width σ. Full cross-validation has been applied to each model independently, to find the best λ, λ1, λ2 (see Appendix E). |