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Assassin: an Automatic claSSificAtion system baSed on algorithm SelectIoN

Summary: Assassin automates classifier and hyperparameter selection by combining meta-learning with a reinforcement-trained policy that transfers experience across tasks. Genetic search then tunes the chosen model, achieving strong OpenML performance with user-configurable search. (summarized by gpt-5.6-luna on Jul 24 2026)

Paper ID
hccfa35cc155820d3
Venue
VLDB
Year
2021
Pagerank
5.2886806e-05
Overall Rank
8,741 | 41.24%
DOI
10.14778/3476311.3476336

Incoming Non-self Citations Over Time

Authors

BibTeX Citation

@article{mu_vldb21,
        title = {{Assassin: an Automatic claSSificAtion system baSed on algorithm SelectIoN}},
        author = {Mu, Tianyu and Wang, Hongzhi and Zheng, Shenghe and Zhang, Shaoqing and Liang, Cheng and Tang, Haoyun},
        journal = {PVLDB},
        series = {{VLDB} '21},
        volume = {14},
        number = {12},
        pages = {2751--2754},
        doi = {10.14778/3476311.3476336},
        url = {https://doi.org/10.14778/3476311.3476336},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank Citing Paper Year Venue Pagerank
7,275 SubStrat: A Subset-Based Optimization Strategy for Faster AutoML 2023 VLDB 5.5679786e-05
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Outgoing Citations (Sorted by Pagerank)

Showing 1 of 1 cited papers.

Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.

Rank Cited Paper Year Venue Pagerank
3,067 Oracle AutoML: A Fast and Predictive AutoML Pipeline 2020 VLDB 7.6876681e-05
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