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].
Approximate Knowledge Compilation by Online Collapsed Importance Sampling
Authors: Tal Friedman, Guy Van den Broeck
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation shows that collapsed compilation performs well on standard benchmarks. |
| Researcher Affiliation | Academia | Tal Friedman Computer Science Department University of California Los Angeles, CA 90095 EMAIL Guy Van den Broeck Computer Science Department University of California Los Angeles, CA 90095 EMAIL |
| Pseudocode | Yes | Algorithm 1: Online Collapsed IS |
| Open Source Code | Yes | We provide an open-source Scala implementation of this collapsed compilation algorithm.1 The code is available at https://github.com/UCLA-Star AI/Collapsed-Compilation. It uses the SDD library for knowledge compilation (Darwiche, 2011) and the Scala interface by Bekker et al. (2015). |
| Open Datasets | Yes | From the 2014 UAI inference competition, we evaluate on linkage(1077,1077), Grids(100,300), DBN(40, 440), and Segmentation(228,845) problem instances. From the 2008 UAI inference competition, we use two semi-deterministic grid instances, 50-20(400, 400) and 75-26(676, 676). |
| Dataset Splits | No | No specific dataset split information for training or validation was provided. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) were provided for running experiments. |
| Software Dependencies | No | The code is available at https://github.com/UCLA-Star AI/Collapsed-Compilation. It uses the SDD library for knowledge compilation (Darwiche, 2011) and the Scala interface by Bekker et al. (2015). |
| Experiment Setup | Yes | For evaluation, we run all sampling-based methods 5 times for 1 hour each. We report the median Hellinger distance across all runs... and ...we compare the performance on three different settings for the circuit size threshold: 10,000, 100,000, and 1,000,000. To constrain EDBP, we limit the corresponding circuit size for the junction tree used. In our experiments we set these limits at 100,000 and 1,000,000. To constrain SS, we limit treewidth w at either 15, 12, or 10. |