Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis

Summary: Shows that approximate computation can implicitly provide statistical regularization, bridging algorithmic database methods and statistical robustness for noisy, very-large-scale data. Case studies (theoretical and empirical) demonstrate principled approximation yields scalable algorithms with improved inferential and predictive properties. (summarized by gpt-5-mini on Feb 09 2026)

Paper ID: 1572
Venue: PODS
Year: 2012
Pagerank: 5.093636e-05
Overall Rank: 12,304 | 15.59%
DOI: 10.1145/2213556.2213579

Incoming Non-self Citations Over Time

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

Authors

1. Michael W. Mahoney (Stanford University)

BibTeX Citation

@inproceedings{mahoney_pods12,
        address = {New York, NY, USA},
        series = {{PODS} '12},
        title = {{Approximate Computation and Implicit Regularization for Very Large-scale Data Analysis}},
        url = {https://dl.acm.org/doi/10.1145/2213556.2213579},
        doi = {10.1145/2213556.2213579},
        booktitle = {Proceedings of the {ACM} {SIGMOD} Symposium on {Principles} of {Database} {Systems}},
        publisher = {Association for Computing Machinery},
        author = {Mahoney, Michael W.},
        year = {2012}
}

Incoming Citations (Sorted by Pagerank)

Showing 0 of 0 citing papers.

Rank	Citing Paper	Year	Venue	Pagerank

Outgoing Citations (Sorted by Pagerank)

Showing 4 of 4 cited papers.

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

Rank	Cited Paper	Year	Venue	Pagerank
155	MAD Skills: New Analysis Practices for Big Data	2009	VLDB	0.00028713176
556	Fast Incremental and Personalized PageRank	2011	VLDB	0.00016564032
945	Fast Personalized PageRank on MapReduce	2011	SIGMOD	0.00013066956
1,396	Estimating PageRank on Graph Streams	2008	PODS	0.00010921308

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