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Efficient Second Order Online Learning by Sketching

Neural Information Processing Systems

We propose Sketched Online Newton (SON), an online second order learning algorithm that enjoys substantially improved regret guarantees for ill-conditioned data. SON is an enhanced version of the Online Newton Step, which, via sketching techniques enjoys a running time linear in the dimension and sketch size. We further develop sparse forms of the sketching methods (such as Oja's rule), making the computation linear in the sparsity of features. Together, the algorithm eliminates all computational obstacles in previous second order online learning approaches.


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Mashable

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Spark Project (Prediction Online Shopper Purchase Intention)

#artificialintelligence

Once a user logs into an online shopping website, knowing whether the person will make a purchase or not holds a massive economical value. A lot of current research is focused on real-time revenue predictors for these shopping websites. In this article, we will start building a revenue predictor for one such website. In this Data Science Machine Learning project, we will create a Real-time prediction of online shoppers' purchasing intention Project using Apache Spark Machine Learning Models using Logistic Regression, one of the predictive models. Databricks lets you start writing Spark ML code instantly so you can focus on your data problems.


Efficient Second Order Online Learning by Sketching

Neural Information Processing Systems

We propose Sketched Online Newton (SON), an online second order learning algorithm that enjoys substantially improved regret guarantees for ill-conditioned data. SON is an enhanced version of the Online Newton Step, which, via sketching techniques enjoys a running time linear in the dimension and sketch size. We further develop sparse forms of the sketching methods (such as Oja's rule), making the computation linear in the sparsity of features. Together, the algorithm eliminates all computational obstacles in previous second order online learning approaches. Papers published at the Neural Information Processing Systems Conference.


Announcing AWS Machine Learning Research Awards Amazon Web Services

#artificialintelligence

We are working with Carnegie Mellon University, California Institute of Technology (Caltech), Harvard Medical School, The University of Washington, and the University of California, Berkeley on this program.