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].
Tractable Regularization of Probabilistic Circuits
Authors: Anji Liu, Guy Van den Broeck
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that both methods consistently improve the generalization performance of a wide variety of PCs. Moreover, when paired with a simple PC structure, we achieved state-of-the-art results on 10 out of 20 standard discrete density estimation benchmarks. Open-source code and experiments are available at https://github.com/UCLA-Star AI/Tractable-PC-Regularization. |
| Researcher Affiliation | Academia | Anji Liu Department of Computer Science UCLA Los Angeles, CA 90095 EMAIL Guy Van den Broeck Department of Computer Science UCLA Los Angeles, CA 90095 EMAIL |
| Pseudocode | Yes | Algorithm 1 Forward pass ... Algorithm 2 Backward pass ... Algorithm 3 PC Entropy regularization |
| Open Source Code | Yes | Open-source code and experiments are available at https://github.com/UCLA-Star AI/Tractable-PC-Regularization. |
| Open Datasets | Yes | Empirical evaluation We empirically evaluate both proposed regularization methods on the twenty density estimation datasets [39]... We first examine the performance on a protein sequence dataset [29] that suffers from severe overfitting. |
| Dataset Splits | No | While the paper mentions the use of 'validation set' multiple times (e.g., 'using the validation set and report results on the test set'), it does not provide specific details on how these splits were performed (e.g., percentages or sample counts for training, validation, and test sets in the main text). |
| Hardware Specification | No | The main text of the paper does not specify any hardware details. While the checklist mentions 'Details about computing resources can be found in Appendix B.3', this appendix is not provided in the given text. |
| Software Dependencies | No | The paper does not provide specific software names with version numbers in the main text. While the checklist mentions 'All details for reproducibility are specified in Appendices B.3 and B.4', these appendices are not provided in the given text. |
| Experiment Setup | Yes | For all experiments, we performed a hyperparameter search for all three regularization approaches (Laplace smoothing, data softening, and entropy regularization)5 using the validation set and report results on the test set. ... 5Specifically, α {0.1, 0.4, 1.0, 2.0, 4.0, 10.0}, β {0.9996, 0.999, 0.996}, τ {0.001, 0.01, 0.1}. ... For all experiments, we trained the PCs with 100 mini-batch EM epochs and 100 full-batch EM epochs. |