Two Birds with One Stone: Efficient Deep Learning over Mislabeled Data through Subset Selection
Summary: Deem selects a subset under label uncertainty by using losses and gradients to approximate the full gradient on soft labels. Framed as submodular NP-hard subset selection with a scalable approximation, it yields up to 10× speedups with no accuracy loss. (summarized by gpt-5-nano on Feb 09 2026)
Incoming Non-self Citations Over Time
No non-self incoming citations found for this paper in this database.
Authors
- 1. Yuhao Deng (Beijing Institute of Technology)
- 2. Chengliang Chai (Beijing Institute of Technology)
- 3. Kaisen Jin (Beijing Institute of Technology)
- 4. Linan Zheng (University of Arizona)
- 5. Lei Cao (Massachusetts Institute of Technology; University of Arizona)
- 6. Ye Yuan (Beijing Institute of Technology)
- 7. Guoren Wang (Beijing Institute of Technology)
BibTeX Citation
@inproceedings{deng_sigmod25,
title = {{Two Birds with One Stone: Efficient Deep Learning over Mislabeled Data through Subset Selection}},
author = {Deng, Yuhao and Chai, Chengliang and Jin, Kaisen and Zheng, Linan and Cao, Lei and Yuan, Ye and Wang, Guoren},
series = {{SIGMOD} '25},
booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
publisher = {Association for Computing Machinery},
doi = {10.1145/3728289},
url = {https://dl.acm.org/doi/10.1145/3728289},
year = {2025}
}
Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 10,821 | Data-efficient Online Training for Direct Alignment in LLMs | 2026 | VLDB | 4.9793485e-05 |
Previous
Page 1 / 1
Next
Outgoing Citations (Sorted by Pagerank)
Showing 6 of 6 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
| Rank | Cited Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 15 | How Good Are Query Optimizers, Really? | 2016 | VLDB | 0.00061066921 |
| 483 | ActiveClean: Interactive Data Cleaning For Statistical Modeling | 2016 | VLDB | 0.00017590977 |
| 1,847 | Nearest Neighbor Classifiers over Incomplete Information: From Certain Answers to Certain Predictions | 2021 | VLDB | 9.5120573e-05 |
| 3,941 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD | 6.9138042e-05 |
| 7,201 | Coresets over Multiple Tables for Feature-rich and Data-efficient Machine Learning | 2023 | VLDB | 5.5871656e-05 |
| 7,648 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB | 5.4772833e-05 |
Previous
Page 1 / 1
Next
Semantically Similar Papers
| # | Overall Rank | Paper | Year | Venue |
|---|---|---|---|---|
| 1 | 7,417 | Cardinality Estimation of Approximate Substring Queries using Deep Learning | 2022 | VLDB |
| 2 | 8,639 | Efficient Summarization Framework for Multi-Attribute Uncertain Data | 2014 | SIGMOD |
| 3 | 5,126 | Monotonic Cardinality Estimation of Similarity Selection: A Deep Learning Approach | 2020 | SIGMOD |
| 4 | 3,941 | GoodCore: Data-effective and Data-efficient Machine Learning through Coreset Selection over Incomplete Data | 2023 | SIGMOD |
| 5 | 13,579 | Effective Clustering for Large Multi-Relational Graphs | 2026 | SIGMOD |
| 6 | 11,514 | Certain and Approximately Certain Models for Statistical Learning | 2024 | SIGMOD |
| 7 | 7,648 | MisDetect: Iterative Mislabel Detection using Early Loss | 2024 | VLDB |
| 8 | 1,580 | Deep Learning Models for Selectivity Estimation of Multi-Attribute Queries | 2020 | SIGMOD |
| 9 | 8,276 | Consistent and Flexible Selectivity Estimation for High-Dimensional Data | 2021 | SIGMOD |
| 10 | 9,333 | Deep Query Optimization | 2019 | SIGMOD |