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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
On Tractable Computation of Expected Predictions
Authors: Pasha Khosravi, YooJung Choi, Yitao Liang, Antonio Vergari, Guy Van den Broeck
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We empirically show our algorithm to consistently outperform standard imputation techniques on a variety of datasets. |
| Researcher Affiliation | Academia | Pasha Khosravi, Yoo Jung Choi, Yitao Liang, Antonio Vergari, and Guy Van den Broeck Department of Computer Science University of California, Los Angeles EMAIL |
| Pseudocode | Yes | Algorithm 1 EC2(n, m) |
| Open Source Code | Yes | Our implementation of the algorithm and experiments are available at https://github.com/UCLA-Star AI/mc2. |
| Open Datasets | Yes | We construct a 6-dataset testing suite, four of which are common regression benchmarks from several domains [13], and the rest are classification on MNIST and FASHION datasets [36, 35]. |
| Dataset Splits | No | No specific details on train/validation/test splits (e.g., percentages or exact counts) are provided. The paper mentions monitoring loss on a 'held out set' during structure learning and using 'different percentages of missing features' for evaluation, but not specific train/validation splits. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for experiments are mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch x.x) are listed in the paper. |
| Experiment Setup | Yes | For RCs, we adapt the parameter and structure learning of LCs [18], substituting the logistic regression objective with a ridge regression during optimization. For structure learning of both LCs and RCs, we considered up to 100 iterates while monitoring the loss on a held out set. For PSDDs we employ the parameter and structure learning of [19] with default parameters and run it up to 1000 iterates until no significant improvement is seen on a held out set. |