Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
Authors: Victor-Emmanuel Brunel
NeurIPS 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In Section 3, we propose a solution to the principal minor assignment problem for signed kernels, which yields a polynomial time learning algorithm for the kernel of a signed DPP. Theorem 3. Algorithm 1 finds a solution of (PMA 1) in polynomial time in N. |
| Researcher Affiliation | Academia | Victor-Emmanuel Brunel Department of Mathematics Massachusetts Institute of Technology Cambridge, MA 02139 vebrunel@mit.edu |
| Pseudocode | Yes | Algorithm 1 Find a solution H to (PMA 1) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific dataset for training, therefore no dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments involving dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not report on experimental hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not list specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details such as hyperparameter values or training configurations. |