VLDB 2026 Research / reviewers in the wild / expert
Shaowei Kou
dblp:164/4521
· DBLP profile ↗
7ranked-venue papers
0as first author
2since 2021 · last 2023
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 7 · 2 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2023 | A 5k-vertex kernel for 3-path vertex cover
Mingyu Xiao 0001, Shaowei Kou |
Theor. Comput. Sci. | 2 |
| 2022 | A simple and improved parameterized algorithm for bicluster editing
Mingyu Xiao 0001, Shaowei Kou |
Inf. Process. Lett. | 2 |
| 2020 | Parameterized algorithms and kernels for almost induced matching
Mingyu Xiao 0001, Shaowei Kou |
Theor. Comput. Sci. | 2 |
| 2017 | Kernelization and Parameterized Algorithms for 3-Path Vertex Cover
Mingyu Xiao 0001, Shaowei Kou |
TAMC | 2 |
| 2017 | Exact algorithms for the maximum dissociation set and minimum 3-path vertex cover problems
Mingyu Xiao 0001, Shaowei Kou |
Theor. Comput. Sci. | 2 |
| 2016 | An Improved Approximation Algorithm for the Traveling Tournament Problem with Maximum Trip Length TwoabstractThe Traveling Tournament Problem is a complex combinatorial optimization problem in tournament timetabling, which asks a schedule of home/away games meeting specific feasibility requirements, while also minimizing the total distance traveled by all the n teams (n is even). Despite intensive algorithmic research on this problem over the last decade, most instances with more than 10 teams in well-known benchmarks are still unsolved. In this paper, we give a practical approximation algorithm for the problem with constraints such that at most two consecutive home games or away games are allowed. Our algorithm, that generates feasible schedules based on minimum perfect matchings in the underlying graph, not only improves the previous approximation ratio from (1+16/n) to about (1+4/n) but also has very good experimental performances. By applying our schedules on known benchmark sets, we can beat all previously-known results of instances with n being a multiple of 4 by 3% to 10%. Mingyu Xiao 0001, Shaowei Kou |
MFCS | 2 |
| 2016 | Almost Induced Matching: Linear Kernels and Parameterized Algorithms
Mingyu Xiao 0001, Shaowei Kou |
WG | 2 |