Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study

Summary: Proposes Declarative Feature Selection (DFS) to enforce multi-constraint ML systems (fairness, privacy, latency). Benchmarking of feature-selection algorithms and a meta-learning optimizer yield guidance on when to use which strategy, model-agnostic. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6247
Venue: SIGMOD
Year: 2021
Pagerank: 5.093636e-05
Overall Rank: 11,674 | 19.91%
DOI: 10.1145/3448016.3457295

Incoming Non-self Citations Over Time

No non-self incoming citations found for this paper in this database.

Authors

1. Felix Neutatz (Technical University of Berlin)
2. Felix Biessmann (Beuth University Berlin; Einstein Center Digital Future Berlin)
3. Ziawasch Abedjan (L3S Research Center; Leibniz University Hanover)

BibTeX Citation

@inproceedings{neutatz_sigmod21,
        title = {{Enforcing Constraints for Machine Learning Systems via Declarative Feature Selection: An Experimental Study}},
        author = {Neutatz, Felix and Biessmann, Felix and Abedjan, Ziawasch},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3457295},
        url = {https://dl.acm.org/doi/10.1145/3448016.3457295},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
3,905	Automated Feature Engineering for Algorithmic Fairness	2021	VLDB	7.029145e-05

Outgoing Citations (Sorted by Pagerank)

Showing 15 of 15 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
415	SystemML: Declarative Machine Learning on Spark	2016	VLDB	0.0001888524
640	Materialization Optimizations for Feature Selection Workloads	2014	SIGMOD	0.00015409494
764	To Join or Not to Join? Thinking Twice about Joins before Feature Selection	2016	SIGMOD	0.00014226652
863	Interventional Fairness : Causal Database Repair for Algorithmic Fairness	2019	SIGMOD	0.00013531835
1,004	Democratizing Data Science through Interactive Curation of ML Pipelines	2019	SIGMOD	0.00012701932
1,250	Data Management in Machine Learning: Challenges, Techniques, and Systems	2017	SIGMOD	0.00011485301
1,340	Responsible Data Management	2020	VLDB	0.00011111667
1,569	HELIX: Holistic Optimization for Accelerating Iterative Machine Learning	2019	VLDB	0.00010335423
1,756	SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle	2020	CIDR	9.8172465e-05
2,604	Brainwash: A Data System for Feature Engineering	2013	CIDR	8.3524514e-05
3,012	Oracle AutoML: A Fast and Predictive AutoML Pipeline	2020	VLDB	7.8519448e-05
5,297	An Integrated Development Environment for Faster Feature Engineering	2014	VLDB	6.2772486e-05
5,699	Optimizing Machine Learning Workloads in Collaborative Environments	2020	SIGMOD	6.1170243e-05
7,000	A Cost-based Optimizer for Gradient Descent Optimization	2017	SIGMOD	5.7287645e-05
11,744	CAFE: Constraint-Aware Feature Extraction from Large Databases	2020	CIDR	5.093636e-05

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