ICML 2016 in NYC and KDD Cup 2016

ICML 2016 in NYC and KDD Cup 2016 ICML 2016 is in New York City. I expect it to be the largest ICML by far given the destination—New York is the place which is perhaps easiest to reach from anywhere in the world and it has the largest machine learning meetup anywhere in the world. I am the general chair…
Original post: ICML 2016 in NYC and KDD Cup 2016
Source: Machine Learning (Theory)

How to measure translation quality in your user interfaces

Posted by Javier Bargas-Avila, User Experience Research at GoogleWorldwide, there are about 200 languages that are spoken by at least 3 million people. In this global context, software developers are required to translate their user interfaces into many languages. While graphical user interfaces have evolved substantially when compared to text-based user interfaces, they still rely heavily on textual information. The…
Original post: How to measure translation quality in your user interfaces
Source: Google Research

Data scientist as scientist

by NIALL CARDIN      OMKAR MURALIDHARAN      AMIR NAJMI When working with complex systems or phenomena, the data scientist must often operate with incomplete and provisional understanding, even as she works to advance the state of knowledge. This is very much what scientists do. Our post describes how we arrived at recent changes to design principles for the…
Original post: Data scientist as scientist
Source: Unofficial Google Data Science

Data Workflows with Erik Andrejko from Climate Corporation

The best data science teams operate as far more than the sum of their parts. Instead of working in independent silos, a data scientist on one of these teams leverages her colleagues’ ideas, code, and intermediate data to lay the groundwork for her projects. Efficient workflows for sharing and collaborating on code and data are crucial for this. On Kaggle,…
Original post: Data Workflows with Erik Andrejko from Climate Corporation
Source: Kaggle

KDD Cup 2016 CFP

The KDD Cup is soliciting ideas for their next competition. Things have gotten tricky for the KDD Cup, because CJ’s class keeps winning. Essentially we have learned that lots of feature engineering and large ensembles do well in supervised learning tasks. But really CJ has done us a favor by directly demonstrating that certain types of supervised learning are extremely…
Original post: KDD Cup 2016 CFP
Source: Machined Learnings

Improving YouTube video thumbnails with deep neural nets

Posted by Weilong Yang and Min-hsuan Tsai, Video Content Analysis team and the YouTube Creator teamVideo thumbnails are often the first things viewers see when they look for something interesting to watch. A strong, vibrant, and relevant thumbnail draws attention, giving viewers a quick preview of the content of the video, and helps them to find content more easily. Better…
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Experiment Design and Modeling for Long-term Studies in Ads

by HENNING HOHNHOLD     DEIRDRE O’BRIEN     DIANE TANG In this post we discuss the challenges in measuring and modeling the long-term effect of ads on user behavior. We describe experiment designs which have proven effective for us and discuss the subtleties of trying to generalize the results via modeling. A/B testing is used widely in information technology companies to…
Original post: Experiment Design and Modeling for Long-term Studies in Ads
Source: Unofficial Google Data Science