Recovery of sparse linear classifiers from mixture of responses
Authors: Venkata Gandikota, Arya Mazumdar, Soumyabrata Pal
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Moreover, we demonstrate the capability of our algorithms to learn movie genre preferences of two unknown users using the Movie Lens [18] dataset. The experimental details are included in Section E of the supplementary material. |
| Researcher Affiliation | Academia | Venkata Gandikota University of Massachusetts Amherst, MA Amherst, MA 01003 gandikota.venkata@gmail.com Arya Mazumdar University of Massachusetts Amherst, MA Amherst, MA 003 arya@cs.umass.edu Soumyabrata Pal University of Massachusetts Amherst, MA Amherst, MA 01003 soumyabratap@umass.edu |
| Pseudocode | Yes | Algorithm 1 QUERY(v, T) ... Algorithm 2 RECOVER SUPPORT ... Algorithm 3 ESTIMATE SIZES OF S(i) (SUPPORT RECOVERY) ... Algorithm 4 ESTIMATE SIZES OF S(i) [ S(j) (INTERSECTION RECOVERY) |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that source code for the described methodology is publicly available. |
| Open Datasets | Yes | Moreover, we demonstrate the capability of our algorithms to learn movie genre preferences of two unknown users using the Movie Lens [18] dataset. |
| Dataset Splits | No | The paper mentions using the Movie Lens dataset and that experimental details are in the supplementary material, but it does not specify any training, validation, or test splits in the main text. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers used in the experiments. |
| Experiment Setup | No | The paper states that 'The experimental details are included in Section E of the supplementary material.' but does not provide specific experimental setup details such as hyperparameters or system-level training settings in the main text. |