Jennifer Paykin

Artificial Intelligence · 2011

A researcher in the Science corpus: 1 paper; trusts 3 researchers and is trusted by 2 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 11 citations into it; #29,209 of 35,345 by trust here, #25,809 by citations
• Information Systems — 10 citations into it
• Information Retrieval & Search — 5 citations into it

Whom Jennifer Paykin trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Leo Breiman
2. Geoffrey I. Webb

Who trusts Jennifer Paykin overall

1. Carlos Guestrin
2. Tianqi Chen

Papers from Jennifer Paykin's perspective

Ordered by how strongly the researchers Jennifer Paykin trusts endorse each paper. Same corpus, a different order for every researcher.

1. Bagging Predictors
2. Random Forests
3. Large Margin Rank Boundaries for Ordinal Regression
4. Learning to rank using gradient descent
5. MultiBoosting: A Technique for Combining Boosting and Wagging (Machine-mediated learning)
6. From RankNet to LambdaRank to LambdaMART: An Overview
7. Yahoo! Learning to Rank Challenge Overview
8. Stochastic gradient boosted distributed decision trees
9. PLANET: Massively Parallel Learning of Tree Ensembles with MapReduce
10. The Nature of Statistical Learning Theory

Their papers

• Parallel boosted regression trees for web search ranking (2011) — 190 citations