Have you considered taking a look at the book Visible Learning by John Hattie? It seems to be permeating and informing reform in our K-12 schools nationwide. Districts are spending a lot of money sending their staffs to conferences by Solution Tree to train their schools to become PLC communities which also use an RTI (Response To Intervention) model. Their powerpoint presentations prominently feature John Hattie’s work. Down the chain, then, if all of these school districts attending are like mine, their superintendents, assistant superintendents, principals, and vice principals are constantly quoting John Hattie’s work to support their initiatives, because they clearly see it as a powerful tool.

“We now have unsupervised techniques that actually work. The problem is that you can beat them by just collecting more data, and then using supervised learning. This is why in industry, the applications of Deep Learning are currently all supervised. I agree with you that for the search and advertising industry, supervised learning is used because of the vast amounts of data being generated and gathered.” Yann LeCun ( 2015 )

Learning a Policy for Opportunistic Active Learning

Receding Horizon Gradient Descent (RHGD) This paper studies an online optimization problem with switching costs and a finite prediction window. We propose two computationally efficient algorithms: Receding Horizon Gradient Descent (RHGD), and Receding Horizon Accelerated Gradient (RHAG). Both algorithms only require a finite number of gradient evaluations at each time. We show that both the dynamic regret and the competitive ratio of the proposed algorithms decay exponentially fast with the length of the prediction window, and the decay rate of RHAG is larger than RHGD. Moreover, we provide a fundamental lower bound on the dynamic regret for general online algorithms with a finite prediction window. The lower bound matches the dynamic regret of our RHAG, meaning that the performance can not improve significantly even with more computation. Lastly, we present simulation results to test our algorithms numerically. …

R tip : how to pass a formula to lm().

A Faster Unique Function (funique)Similar to base’s unique function, only optimized for working with data frames, especially those that contain date-time columns.

More on Security Data Lakes – And FAIL!

Max Kasy and Isaiah Andrews sent along this paper, which begins:

Amazon SageMaker is an end-to-end platform used by data scientists and developers to build, train, tune, and deploy machine learning (ML) models at scale. Amazon SageMaker lets you begin training your model with a single click in the console or with a simple API call. When the training is complete, and you’re ready to deploy your model, you can launch it with a single click in the Amazon SageMaker console. After you deploy a model into production using the Amazon SageMaker hosting service, you have a persistent HTTPS endpoint where your machine learning model is available to provide inferences via the InvokeEndpoint API action.