Sound Abstraction and Decomposition of Probabilistic Programs
Authors: Steven Holtzen, Guy Broeck, Todd Millstein
ICML 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimentally, we also illustrate the practical benefits of our framework as a tool to decompose probabilistic program inference. |
| Researcher Affiliation | Academia | 1University of California, Los Angeles. Correspondence to: Steven Holtzen <sholtzen@cs.ucla.edu>, Guy Van den Broeck <guyvdb@cs.ucla.edu>, Todd Millstein <todd@cs.ucla.edu>. |
| Pseudocode | Yes | Algorithm 1 Domain completion |
| Open Source Code | No | The paper mentions using third-party tools like 'Psi4 (Gehr et al., 2016)' and 'Web PPL (Goodman & Stuhlmüller, 2014)', but it does not provide an explicit statement or link for the open-source code of their own methodology. |
| Open Datasets | No | The paper describes programs used as examples for experiments (e.g., Multiplication, Markov Chain, Shuffle, and a program with noisy observations), but it does not refer to them as publicly available datasets nor does it provide concrete access information (links, DOIs, repositories, or formal citations) for them. |
| Dataset Splits | No | The paper describes various experimental setups but does not specify dataset split information (e.g., percentages, sample counts, or references to predefined splits) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | Yes | We utilized build 5334524fe. |
| Experiment Setup | No | The paper describes the probabilistic programs and inference methods, but it does not provide specific experimental setup details such as hyperparameters (e.g., learning rates, batch sizes, number of epochs) or specific optimizer settings for the MCMC experiments. |