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..
Learning Signed Determinantal Point Processes through the Principal Minor Assignment Problem
Authors: Victor-Emmanuel Brunel
NeurIPS 2018 | Venue PDF | 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 ο¬nds 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 EMAIL |
| 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. |