Scalable MCMC Sampling for Nonsymmetric Determinantal Point Processes
Authors: Insu Han, Mike Gartrell, Elvis Dohmatob, Amin Karbasi
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | With both a theoretical analysis and experiments on realworld datasets, we verify that our scalable approximate sampling algorithms are orders of magnitude faster than existing sampling approaches for k-NDPPs and NDPPs. |
| Researcher Affiliation | Collaboration | Insu Han 1 Mike Gartrell 2 Elvis Dohmatob 3 Amin Karbasi 1 1Yale University 2Criteo AI Lab, Paris, France 3Facebook AI Lab, Paris, France. Correspondence to: Insu Han <insu.han@yale.edu>, Mike Gartrell <m.gartrell@criteo.com>. |
| Pseudocode | Yes | Algorithm 1 MCMC Sampling for k-NDPP; Algorithm 2 Up Operator via Rejection Sampling; Algorithm 3 Tree-based k-DPP Sampling; Algorithm 4 MCMC Sampling for NDPP |
| Open Source Code | Yes | The source code for our NDPP sampling algorithms is publicly available at https://github.com/insuhan/ndpp-mcmc-sampling. |
| Open Datasets | Yes | UK Retail: This dataset (Chen et al., 2012); Recipe: This dataset (Majumder et al., 2019); Instacart: This dataset (Instacart, 2017); Million Song: This dataset (Mc Fee et al., 2012); Book: This dataset (Wan & Mc Auley, 2018) |
| Dataset Splits | Yes | We use the training scheme from (Han et al., 2022), where 300 randomly-selected baskets are held-out as a validation set for tracking convergence during training, another 2000 random subsets are used for testing, and the remaining baskets are used for training. |
| Hardware Specification | No | No specific hardware details (e.g., CPU/GPU models, memory, or specific cloud instances) were explicitly provided for running the experiments. |
| Software Dependencies | No | The paper mentions using 'Adam optimizer (Kingma & Ba, 2015)' but does not specify software versions for libraries or programming languages. |
| Experiment Setup | Yes | We use the Adam optimizer (Kingma & Ba, 2015); we initialize D from N(0, 1), and V and B are initialized from the U([0, 1]). We set α = β = 0.01 for all datasets. |