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 [1].
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. |