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].
Expectation-Maximization for Learning Determinantal Point Processes
Authors: Jennifer A Gillenwater, Alex Kulesza, Emily B. Fox, Ben Taskar
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on a real-world product recommendation task, and achieve relative gains of up to 16.5% in test log-likelihood compared to the naive approach of maximizing likelihood by projected gradient ascent on the entries of the kernel matrix. |
| Researcher Affiliation | Academia | Jennifer Gillenwater Computer and Information Science University of Pennsylvania EMAIL Alex Kulesza Computer Science and Engineering University of Michigan EMAIL Emily Fox Statistics University of Washington EMAIL Ben Taskar Computer Science and Engineering University of Washington EMAIL |
| Pseudocode | Yes | Algorithm 1 K-Ascent (KA) and Algorithm 2 Expectation-Maximization (EM) are presented. |
| Open Source Code | Yes | Code and data for all experiments can be downloaded from https://code.google.com/p/em-for-dpps |
| Open Datasets | No | To test our DPP learning algorithms, we collected a dataset consisting of 29,632 baby registries from Amazon.com, ๏ฌltering out those listing fewer than 5 or more than 100 products. |
| Dataset Splits | No | The paper states: "We used 70% of the data for training and 30% for testing." It does not specify a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers. |
| Experiment Setup | Yes | The paper describes experimental setup details such as two initialization types (Wishart distribution and moments-matching), a 70% training and 30% testing data split, and data filtering criteria (e.g., 'filtering out those listing fewer than 5 or more than 100 products', 'filtered down to its top 100 most frequent items'). |