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: 6058
Venue: SIGMOD
Year: 2021
Pagerank: 4.2856106e-05
Overall Rank: 9,773 | 32.02%
DOI: 10.1145/3448016.3452777

Incoming Non-self Citations Over Time

Authors

1. An Yan
2. Bill Howe

Incoming Citations (Sorted by Pagerank)

Showing 1 of 1 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
11,216	Demystifying the QoS and QoE of Edge-hosted Video Streaming Applications in the Wild with SNESet	2023	SIGMOD	4.1945683e-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
939	Data Lake Management: Challenges and Opportunities	2019	VLDB	0.00015187344
2,104	Data Polygamy: The Many-Many Relationships among Urban Spatio-Temporal Data Sets	2016	SIGMOD	9.536298e-05
2,730	Open Data Integration	2018	VLDB	8.2126735e-05

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