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..
On Separability of Loss Functions, and Revisiting Discriminative Vs Generative Models
Authors: Adarsh Prasad, Alexandru Niculescu-Mizil, Pradeep K. Ravikumar
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We instantiate our results with two running examples of isotropic and non-isotropic Gaussian generative models, and also corroborate our theory with instructive simulations. and 6 Experiments: High Dimensional Classification |
| Researcher Affiliation | Collaboration | Adarsh Prasad Machine Learning Dept. CMU EMAIL, Alexandru Niculescu-Mizil NEC Laboratories America Princeton, NJ, USA EMAIL, Pradeep Ravikumar Machine Learning Dept. CMU EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any information about open-source code availability for the described methodology. |
| Open Datasets | No | For our experimental setup, we consider isotropic Gaussian models with means µ0 = 1p 1 ps , µ1 = 1p + 1 ps , and vary the sparsity level s. |
| Dataset Splits | No | The paper describes generating data for simulations and averaging results over 20 trials, rather than using explicit training/validation/test splits from a fixed dataset. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For both methods, we set the regularization parameter 2 as λn = log(p)/n. and we introduce a thresholded generative estimator that has two stages: (a) compute b diff using the generative model estimates, and (b) soft-threshold the generative estimate with λn = c n for some constant c. |