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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
1573
Venue
PODS
Year
2012
Pagerank
4.1905499e-05
Overall Rank
12,115 | 15.80%
DOI
-

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

Rank Cited Paper Year Venue Pagerank
169 MAD Skills: New Analysis Practices for Big Data 2009 VLDB 0.00038967371
478 Fast Incremental and Personalized PageRank 2011 VLDB 0.00022183187
589 Estimating PageRank on Graph Streams 2008 PODS 0.00019569121
875 Fast Personalized PageRank on MapReduce 2011 SIGMOD 0.00015679931
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