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73 Things Publishers Do (2013 Edition)
via stephenslighthouse

73 Things Publishers Do (2013 Edition) http://scholarlykitchen.sspnet.org/2013/10/22/updated-73-things-publishers-do-2013-edition/ All of the thousands of librarians who work in publishing will appreciate this updated list from Scholarly Kitchen...
The One Obligatory Sloan "Research" Post
via boxscoregeeks

The Sloan Analytics conference took place last week. In the past, I've attended and enjoyed myself. When it comes to recognizing the value of Sloan, it's important to ask what it is. I view Sloan as a "sports geek comic-con" like experience. Lots of...
Direct Answers: Extracting Text from Pages Citations
via bytelan

This is the last post in a series about Google’s International patent application Natural Language Search Results for Intent Queries. This section was inspired by the citations list at the end of a paper used by the listed inventors as a provisional...
Gaël Varoquaux: Of software and Science. Reproducible science: what, why, and how
via planet.python

At MLOSS 15 we brainstormed on reproducible science, discussing why we care about software in computer science. Here is a summary blending notes from the discussions with my opinion. “Without engineering, science is not more than philosophy” — the community...
The Exploitative Economics of Academic Publishing
via science.slashdot

v3rgEz sends this piece from the Boston Globe: "Taxpayers in the United States spend $139 billion a year on scientific research, yet much of this research is inaccessible not only to the public, but also to other scientists. This is the consequence of...

Jmlr.org popular pages

Distributed Semi-supervised Learning with Kernel Ridge Regression

Xiangyu Chang, Shao-Bo Lin, Ding-Xuan Zhou; 18(46):1−22, 2017. Abstract This paper provides error analysis for distributed semi- supervised learning with kernel ridge regression (DSKRR) based on a d...
Journal of Machine Learning Research

Journal of Machine Learning Research The Journal of Machine Learning Research (JMLR), established in 2000, provides an international forum for the electronic and paper publication of high-quality s...

Learning Instrumental Variables with Structural and Non-Gaussianity Assumptions

Ricardo Silva, Shohei Shimizu; 18(120):1−49, 2017. Abstract Learning a causal effect from observational data requires strong assumptions. One possible method is to use instrumental variables, which ...