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)
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| 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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