Presented By O'Reilly and Cloudera
Make Data Work
Sept 29–Oct 1, 2015 • New York, NY
Mikhail Bilenko

Mikhail Bilenko
Principal Researcher, Microsoft


Misha Bilenko is the principal researcher leading the Machine Learning Algorithms team in the Cloud+Enterprise division of Microsoft. Before that, he worked for seven years in the Machine Learning Group at Microsoft Research, where he collaborated with a number of product groups on applied ML algorithms, systems, and tools. Misha joined Microsoft in 2006 after receiving his Ph.D. in computer science from the University of Texas at Austin. He co-edited Scaling Up Machine Learning, published by Cambridge University Press, and his work has received best paper awards from KDD and SIGIR. His research interests include parallel and distributed learning algorithms, accuracy debugging methods, and learnable similarity functions.


9:00am–5:00pm Tuesday, 09/29/2015
Hardcore Data Science
Location: 1 E10 / 1 E11
Ben Lorica (O'Reilly Media), Reza Zadeh (Matroid | Stanford), David Blei (Columbia University), Anima Anandkumar (UC Irvine), Hussein Mehanna (Facebook), Jennifer Chayes (Microsoft Research), Ben Recht (University of California, Berkeley), Tanzeem Choudhury (Cornell and HealthRhythms), Jenn Wortman Vaughan (Microsoft Research), Adam Marcus (B12), Stefanie Jegelka (M.I.T.), Mikhail Bilenko (Microsoft), Reynold Xin (Databricks)
Average rating: ****.
(4.00, 4 ratings)
All-Day: Strata's regular data science track has great talks with real-world experience from leading edge speakers. But we didn't just stop there—we added the Hardcore Data Science day to give you a chance to go even deeper. The Hardcore day will add new techniques and technologies to your data science toolbox, shared by leading data science practitioners from startups, industry, consulting... Read more.
4:00pm–4:30pm Tuesday, 09/29/2015
Hardcore Data Science
Location: 1 E10/1 E11 Level: Intermediate
Mikhail Bilenko (Microsoft)
Average rating: ****.
(4.50, 2 ratings)
Learning with Counts is a simple yet powerful machine learning technique that is widely used in practice, yet has received little attention in literature, remaining a “trick of the trade." This talk will introduce the technique via real-world examples, provide intuition and analysis explaining its power, and describe two extensions yielding significant accuracy and robustness improvements. Read more.