Support Recovery of Sparse Signals from a Mixture of Linear Measurements
Authors: Soumyabrata Pal, Arya Mazumdar, Venkata Gandikota
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide algorithms that use a number of measurements polynomial in k, log n and quasi-polynomial in , to recover the support of all the unknown vectors in the mixture with high probability when each individual component is a k-sparse n-dimensional vector. In Section 3, we provided the detailed proofs of Theorem 1 (Section 3.1), Theorem 3 (Section 3.2) and Theorem 4 (Section 3.3) while deferring the proof of Theorem 2 to Appendix D. |
| Researcher Affiliation | Academia | Venkata Gandikota Electrical Engineering & Computer Science Syracuse University Syracuse, NY 13210 gandikota.venkata@gmail.com; Arya Mazumdar Halıcıoğlu Data Science Institute University of California, San Diego La Jolla, CA 92093 arya@ucsd.edu; Soumyabrata Pal College of Information & Computer Sciences University of Massachusetts Amherst Amherst, MA 01003 soumyabratap@umass.edu |
| Pseudocode | Yes | Algorithm 1 RECOVER p-IDENTIFIABLE SUPPORTS, Algorithm 2 RECOVER FLIP-INDEPENDENT SUPPORTS, Algorithm 3 RECOVER r-KRUSKAL RANK SUPPORTS |
| Open Source Code | No | The paper does not provide any explicit statements about making the source code available or links to a code repository for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on datasets, thus it does not mention public dataset availability for training. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical experiments, therefore it does not discuss training/validation/test dataset splits. |
| Hardware Specification | No | The paper discusses theoretical computational complexity (Remark 5) but does not provide any specific details about the hardware used for computations or experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and proofs, thus it does not include details on experimental setup such as hyperparameters or training configurations. |