Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Fast Relational Probabilistic Inference and Learning: Approximate Counting via Hypergraphs
Authors: Mayukh Das, Devendra Singh Dhami, Gautam Kunapuli, Kristian Kersting, Sriraam Natarajan7816-7824
AAAI 2019 | Venue PDF | LLM Run Details | Input Tokens: 18,563 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,090 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental results demonstrate the efficiency of these approximations, which can be applied to many complex statistical relational models, and can be significantly faster than state-of-the-art, both for inference and learning, without sacrificing effectiveness. We investigate the following questions: (Q1) Is MACH effective and efficient in full model learning with n-ary relations compared to a robust baseline? (Q2) Is modeling n-ary relations faithfully crucial when learning relational model? and (Q3) How does MACH compare (scaling vs. performance) to a state-of-the-art database-centric MLN system? ... Table 1 summarizes the performance and efficiency results of MACH against the baselines for structure and parameter learning of MLNs. |
| Researcher Affiliation | Academia | Mayukh Das University of Texas, Dallas EMAIL Devendra Singh Dhami University of Texas, Dallas EMAIL Gautam Kunapuli University of Texas, Dallas EMAIL Kristian Kersting Technical University of Darmstadt EMAIL Sriraam Natarajan University of Texas, Dallas EMAIL |
| Pseudocode | Yes | Algorithm 1 MACH: Motif-based Approximate Counting via Hypergraphs |
| Open Source Code | Yes | 3Code @ https://github.com/mayukhdas/MACH |
| Open Datasets | Yes | We used three standard SRL data sets: UW-CSE, Citeseer and Web KB, a biomedical data set Carcinogenesis (Srinivasan et al. 1997), and an NLP/Information Extraction(IE) data set NELL-Sports for evaluation. |
| Dataset Splits | No | We computed AUC-ROC, AUC-PR, CLL, F1 and running times averaged over 5 random train/test splits. The paper does not explicitly mention validation dataset splits. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running experiments. |
| Software Dependencies | No | The paper mentions 'Java-based Hypergraph DB architecture' but does not provide specific version numbers for Java or Hypergraph DB, or any other software dependencies. |
| Experiment Setup | No | The paper describes the system and baselines but does not provide specific experimental setup details such as hyperparameter values, model initialization, or training schedules. |