Diane Lambert
Google Publications
-
Collaboration in the Cloud at Google
Yunting Sun, Diane Lambert, Makoto Uchida, Nicolas Remy
research.google.com (2014), pp. 1-13
-
Evaluating Online Ad Campaigns in a Pipeline: Causal Models at Scale
David Chan, Rong Ge, Ori Gershony, Tim Hesterberg, Diane Lambert
Proceedings of ACM SIGKDD 2010, pp. 7-15
-
Diane Lambert, Daryl Pregibon
AdKDD08 (in the ACM digital library), ACM (2008), pp. 10-17
-
A Statistical View of the Transient Signals that Support a Wireless Call
A. Buvaneswari, John M. Graybeal, David A. James, Diane Lambert, Chuanhai Liu, W. Michael MacDonald
Technometrics, vol. 49, no. 3 (2007), pp. 305-317
-
An Algorithm for Fast, Model-Free Tracking Indoors
Aiyou Chen, Christina Harko, Diane Lambert, P. A. Whiting
ACM SIGMOBILE Mobile Computing and Communications Review (2007)
-
More Bang for Their Bucks: Assessing New Features for Online Advertisers
Diane Lambert, Daryl Pregibon
AdKDD07 (in the ACM digital library) (2007)
-
Monitoring Networked Applications with Incremental Quantile Estimation (with discussion)
John M. Chambers, David A. James, Diane Lambert, Scott Vander Wiel
Statistical Science, vol. 21 (2006), pp. 463-475
Previous Publications
-
Adaptive thresholds: Monitoring streams of network counts online
Diane Lambert, Chuanhai Liu
Journal of the American Statistical Association, vol. 101 (2006), pp. 78-89
-
Detecting Fraud in the Real World
Michael Cahill, Fei Chen, Diane Lambert, Jose Pinheiro, Don Sun
Handbook of Massive Datasets, Klewer Academic Publishers (2002), pp. 911-930
-
Estimating Millions of Dynamic Timing Patterns in Real-TIme
Diane Lambert, Jose C. Pinheiro, Don X. Sun
Journal of the American Statistical Associatiooon, vol. 96 (2001), pp. 316-330
-
Mining a stream of transactions for customer patterns
Diane Lambert, José C. Pinheiro
KDD (2001), pp. 305-310
-
Incremental quantile estimation for massive tracking
Fei Chen, Diane Lambert, Jose C. Pinhei
KDD (2000), pp. 516-522
-
What Use is Statistics for Massive Data?
ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery (2000), pp. 54-62