Statistical Learning Inference and Control (SLIC)

Publications and presentations

1. Blaine Nelson, Ira Cohen, “Revisiting probabilistic clustering with pair-wise constraints”, International Conference on Machine Learning (ICML) 2007.
2. Jeffrey Erman, Anirban Mahanti, Martin Arlitt, Ira Cohen, and Carey Williamson, “Semi-Supervised Network Traffic Classification”, Proc. of ACM SIGMETRICS 2007.
3. Chengdu Huang, Ira Cohen, Julie Symons, Tarek Abdelzaher, “Achieving Scalable Automated Diagnosis of Distributed Systems Performance Problems”, HP-Labs Tech Report HPL-2006-160R1.
4. Ira Cohen, Steve Zhang, Moises Goldszmidt, Julie Symons,  Terence Kelly, Armando Fox, “Capturing, Indexing, Clustering, and Retrieving System History”, SOSP 2005. [ Presentation PPT]
5. Rob Powers, Ira Cohen, and Moises Goldszmidt, “Short term performance forecasting in enterprise systems”,  KDD 2005.
6. Moises Goldszmidt, Ira Cohen, Armando Fox and Steve Zhang, “Three research challenges at the intersection of machine learning, statistical induction, and systems”, HOTOS 2005.
7. Steve Zhang, Ira Cohen, Moises Goldszmidt, Julie Symons, Armando Fox, “Ensembles of models for automated diagnosis of system performance problems”,  DSN 2005.
8. Ira Cohen, Moises Goldszmidt, Terence Kelly, Julie Symons, Jeff Chase, “Correlating instrumentation data to system states: A building block for automated diagnosis and control”, OSDI, 2004.
9. George Forman and Ira Cohen, “Beware the null hypothesis”, European Conference on Machine Learning/ European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2005.
10. Ira Cohen and Moises Goldszmidt, “Properties and Benefits of Calibrated Classifiers”, European Conference on Machine Learning/ European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2004.
11. Terence Kelly, Ira Cohen, Moises Goldszmidt and Kimberly Keeton, “Inducing Models of Black-Box Storage Arrays”, HP-Labs Tech Report HPL-2004-108.
12. George Forman and Ira Cohen, “Learning from Little: Comparison of Classifiers given Little Training”, European Conference on Machine Learning/ European Conference on Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD) 2004.

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