survey-YangjiaqiDig created by GitHub Classroom
Process Report:
- Topic Identificaiton(Feb/14/2019):
- topic.pdf is presentation slides.
- 19Spring_Survey_1.pdf is the draft Survey first version.
- version_1.tex is the LaTex code so far, it will be added more contents later.
- Topic exploration(Feb/28/2019):
- topicExploration.pdf is presentation slides.
- Machine Learning of Bayesian Networks Using Constraint Programming.
- Learning Optimal Bayesian Networks Using A* Search.
- Neural Combinatorial Optimization With Reinforcement Learning.
- Survey: The First Draft(Mar/21/2019):
- A Bayesian Method for the Induction of Probabilistic Networks from Data.
- Learning Bayesian Networks is NP-complete.
- Large-smaple Learning Bayesian Networks is NP-hard.
- Probabilistic Network Construction Using the Minimum Description Length Principle.
- Learning Bayesian Networks: Search Methods and Experimental Results.
- Learning Bayesian Network Structure from Massive Datasets: The "Sparse Candidate" Algorithm.
- Ordering-based Search: A Simple and Effective Algorithm for Learning Bayesian Networks.
- Metaheuristics for Score-and-Search Bayesian Network Structure Learning.
- Exact Bayesian Structure Discovery in Bayesian Networks.
- Finding Optimal Bayesian Networks by Dynamic Programming
- Memory-Efficient Dynamic Programming for Learning Optimal Bayesian Network.
- Learning Optimal Bayesian Networks Using A* Search.
- Machine Learning of Bayesian Networks Using Constraint Programming.
- Neural Combinatorial Optimization with Reinforcement Learning.
- Survey: The Second Draft(April/11/2019):
- Learning Bayesian Belief Networks Based on the Minimum Description Length Principle.
- A Branch-and-Bound Algorithm for MDL Learning Bayesian Networks.
- Learning Optimal Bayesian Networks: A Shortest Path Perspective.
- Learning to Discover Sparse Graphical Models.
- Pointer Network.
- Survey: Final Report(May/09/2019):
- Score_Based_Bayesian_Networks_Structure_Learning__A_Survey.pdf