Reliable and Efficient Anytime Skeleton Learning
Authors: Rui Ding, Yanzhi Liu, Jingjing Tian, Zhouyu Fu, Shi Han, Dongmei Zhang10101-10109
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Thorough experiments were conducted on both benchmark and realworld datasets demonstrate that REAL significantly outperforms the other state-of-the-art algorithms. |
| Researcher Affiliation | Collaboration | 1Microsoft Research 2Beijing University of Posts and Telecommunications 3Peking University 4Alibaba Group |
| Pseudocode | Yes | Algorithm 1 Best First Growing Input: priority queue ℚ, variable set 𝒱; Algorithm 2 REAL Input: data 𝐷, variable set 𝒱, cardinalities 𝐶 Output: skeleton 𝐺:; Procedure Prune Input: 𝑇, 𝑅𝑎𝑤𝑃𝐶[𝑇]; Procedure ANDRule Input: 𝑋, 𝑌; Sub-Procedure Robust Corr Input: 𝑇, and its current 𝑅𝑎𝑤𝑃𝐶, 𝑉; Procedure Robust PCFinder Input: 𝑇, and its 𝑅𝑎𝑤𝑃𝐶 Output: A new variable most likely to be a 𝑃𝐶 of 𝑇 |
| Open Source Code | No | The paper states "ALL proofs for lemmas and theorems are available at our website1: 1 https://www.microsoft.com/en-us/research/project/real/", but this does not explicitly state that the source code for the described methodology is available. The paper mentions their implementation in C# but does not provide access. |
| Open Datasets | Yes | We use available benchmark datasets from Bayesian Network Repository (Scutari 2012) for evaluation. |
| Dataset Splits | No | The paper states "We sample 20,000 records from each network." but does not specify explicit training, validation, or test dataset splits or a cross-validation setup. |
| Hardware Specification | Yes | All experiments are conducted on a machine with 3.2GHz Intel i7-8700 processor and 16 GB RAM. |
| Software Dependencies | No | The paper mentions using "C++ implementation of MINOBS", "Java implementation of BLIP", and their own "C# implementation" but does not provide specific version numbers for these languages or any other required libraries. |
| Experiment Setup | Yes | Configurations of MINOBS and BLIP are optimized on these datasets: because all networks are with max-indegree 6 except win95pts is with max-in-degree=7. We set max-in-degree threshold for BLIP and MINOBS to 6 (change it to 7 would significantly degrade their efficiency). |