Incremental Tabular Learning on Heterogeneous Feature Space

Summary: ILEAHE enables incremental tabular learning across evolving, heterogeneous attributes via shared and specific extractors. Discriminative metric guides selecting specific extractors, boosting adaptation to new attributes and preserving past performance. (summarized by gpt-5-nano on Feb 09 2026)

Paper ID: 6521
Venue: SIGMOD
Year: 2023
Pagerank: 4.7865674e-05
Overall Rank: 7,258 | 49.51%
DOI: 10.1145/3588698

Incoming Non-self Citations Over Time

Authors

Incoming Citations (Sorted by Pagerank)

Showing 4 of 4 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank
9,231	Modyn: Data-Centric Machine Learning Pipeline Orchestration	2025	SIGMOD	4.3690661e-05
10,083	GeoKGM: A Multimodal Large Language Model for Zero-Shot Knowledge Graph Completion in Geospatial Databases	2026	SIGMOD	4.1945683e-05
10,100	AixelNet: A Pre-trained Model with Table-aware Adaptation for Structured Data Prediction	2026	SIGMOD	4.1945683e-05
10,233	Efficient GNN Training on Giant Graphs with Collective Batching and Scheduling	2026	VLDB	4.1945683e-05

Outgoing Citations (Sorted by Pagerank)

Showing 0 of 0 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank

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