EquiTensors: Learning Fair Integrations of Heterogeneous Urban Data

Summary: EquiTensors learns shared, fair representations by aligning heterogeneous urban datasets in a spatio-temporal grid and training a convolutional denoising autoencoder. Adaptive weighting with adversarial debiasing reduces dataset dominance and sensitive-attribute leakage, delivering robust, competitive predictions. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: h583f991f702f1fe4
Venue: SIGMOD
Year: 2021
Pagerank: 5.0789354e-05
Overall Rank: 10,111 | 32.02%
DOI: 10.1145/3448016.3452777

Incoming Non-self Citations Over Time

Authors

1. An Yan (University of Washington)
2. Bill Howe (University of Washington)

BibTeX Citation

@inproceedings{yan_sigmod21,
        title = {{EquiTensors: Learning Fair Integrations of Heterogeneous Urban Data}},
        author = {Yan, An and Howe, Bill},
        series = {{SIGMOD} '21},
        booktitle = {Proceedings of the {ACM} {SIGMOD} International Conference on Management of Data},
        publisher = {Association for Computing Machinery},
        doi = {10.1145/3448016.3452777},
        url = {https://dl.acm.org/doi/10.1145/3448016.3452777},
        year = {2021}
}

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,731	Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet	2023	SIGMOD	4.9793485e-05

Outgoing Citations (Sorted by Pagerank)

Showing 3 of 3 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
624	Data Lake Management: Challenges and Opportunities	2019	VLDB	0.00015479826
2,153	Data Polygamy: The Many-Many Relationships among Urban Spatio-Temporal Data Sets	2016	SIGMOD	8.9480694e-05
2,158	Open Data Integration	2018	VLDB	8.941016e-05

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