Hai Xia
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University Assistant, MSc Institute of Logic and Computation Algorithms and Complexity Group TU Wien Room: HA0410 |
Research Interests
Heuristic Search, Satisfiability, Algorithm Selection, Combinatorial Optimization, Evolutionary Optimization
Current Position
I am a PhD student in the Doctoral College Logics for Computer Science at TU Wien (LogiCS@TUWien) that is co-funded by the EC H2020 Marie Skłodowska-Curie COFUND, within the Algorithms and Complexity group under the supervision of Prof. Stefan Szeider.
Education
MSc in Automation at China University of Geosciences, Wuhan, 2021
BSc in Computer Science at China University of Geosciences, Wuhan, 2018
Publications
For an up-to-date list of publications, please visit TU Wien Informatics | Hai Xia.
V. Peruvemba Ramaswamy, S. Szeider, H. Xia, “The Power of Collaboration: Learning Large Bayesian Networks at Scale,” IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Herdon, USA, 2024.
V. Peruvemba Ramaswamy, S. Szeider, H. Xia, “Enhancing MaxSAT-Based Bayesian Network Learning with Real-Time Tuning,” Thirty-Eighth AAAI Workshop on Learnable Optimization (AAAI LEANOPT), Vancouver, Canada, 2024.
H. Xia, S. Szeider, “SAT-Based Tree Decomposition with Iterative Cascading Policy Selection,” Thirty-Eighth AAAI Conference on Artificial Intelligence (AAAI), Vancouver, Canada, 2024, pp. 8191–8199, doi: 10.1609/AAAI.V38I8.28659.
H. Xia, C. Li, Q. Tan, S. Zeng, and S. Yang, “Learning to Search Promising Regions by Space Partitioning for Evolutionary Methods,” Swarm and Evolutionary Computation 91 (2024): 101726, https://doi.org/10.1016/j.swevo.2024.101726.
H. Xia, C. Li, S. Zeng, Q. Tan, J. Wang and S. Yang, “”Learning to Search Promising Regions by a Monte-Carlo Tree Model,”” 2022 IEEE Congress on Evolutionary Computation (CEC), Padua, Italy, 2022, pp. 01-08, doi: 10.1109/CEC55065.2022.9870281.
H. Xia, C. Li, S. Zeng, Q. Tan, J. Wang and S. Yang, “”A Reinforcement-Learning-Based Evolutionary Algorithm Using Solution Space Clustering For Multimodal Optimization Problems,”” 2021 IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, 2021, pp. 1938-1945, doi: 10.1109/CEC45853.2021.9504896.
Q. Tan, C. Li, H. Xia, S. Zeng and S. Yang, “”A Novel Scalable Framework For Constructing Dynamic Multi-objective Optimization Problems,”” 2021 IEEE Congress on Evolutionary Computation (CEC), Kraków, Poland, 2021, pp. 111-118, doi: 10.1109/CEC45853.2021.9504961.
Awards and Scholarships
2nd Place in “Competition on Seeking Multiple Optima in Dynamic Environment” of IEEE WCCI 2022, Jul. 2022
