A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes
Authors: Jennifer Gillenwater, Alex Kulesza, Zelda Mariet, Sergei Vassilvtiskii
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We now present empirical results demonstrating the performance of our sampling algorithms on both synthetic and real-world data sets. |
| Researcher Affiliation | Collaboration | Jennifer Gillenwater 1 Alex Kulesza 1 Zelda Mariet 2 3 Sergei Vassilvitskii 1 1Google Research NYC 2Massachussetts Institute of Technology 3Research performed during an internship at Google. |
| Pseudocode | Yes | Algorithm 1 Dual DPP sampling (Kulesza & Taskar, 2012, Algorithm 3) ... Algorithm 2 Tree Construction ... Algorithm 3 Tree-Based Sampling ... Algorithm 4 Personalized Tree-Based Sampling |
| Open Source Code | No | The paper does not provide any concrete access information (e.g., specific repository link, explicit code release statement, or code in supplementary materials) for the methodology described. |
| Open Datasets | Yes | we build a realistic movie recommender system based on the Movie Lens dataset (Harper & Konstan, 2016) |
| Dataset Splits | No | The paper mentions "held out test data" but does not provide specific details on the training, validation, and test splits (e.g., percentages or counts) needed for reproduction. |
| Hardware Specification | No | The paper mentions general statements like "A modern machine can easily handle millions of items" but does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We use the resulting movie factor matrix as B (N = 26744, D = 30) to define the DPP kernel... using a threshold of ϵ = 0.4... we use the following simple splitting heuristic: we use the items i, j that maximize bi bj to initialize the left and right subtrees; all other items are then greedily added to the left or right tree in order to minimize their (infinity-norm) distance to the initial item. |