Earlier this year, we launched Amazon SageMaker Automatic Model Tuning, which allows developers and data scientists to save significant time and effort in training and tuning their machine learning models. Today, we are launching warm start of hyperparameter tuning jobs in Automatic Model Tuning. Data scientists and developers can now create a new hyperparameter tuning job based on selected parent jobs, so that training jobs conducted in those parent jobs can be reused as prior knowledge. Warm start of hyperparameter tuning jobs will accelerate the hyperparameter tuning process and reduce the cost for tuning models.

Kyle McDonald describes some of the history and current research on using algorithms to generate music. On how David Cope incorporated Markov chains to aid in his work:

If you’re interested in learning more about the implications of the trends discussed in this post, as well as several other trends impacting the analytics market, you can read our trend synopsis here.

At: UnitedHealth GroupLocation: Telecommute, Central or Eastern Time Zones Web: www.unitedhealthgroup.comPosition: Sr Manager, Data Science

Here’s a feature of dplyr that occasionally bites me (most recently while making these graphs). It’s about to change mostly for the better, but is also likely to bite me again in the future. If you want to follow along there’s a GitHub repo with the necessary code and data.

Introduction Domain squatting or URL hijacking is a straightforward attack that requires little skill. An attacker registers a domain that is similar to the target domain and hopes that a user accidentally visits the site. For example, if the domain is example.com, then a typo-squatter would register similar domains such as

At: UnitedHealth GroupLocation: Minnetonka, MN Web: www.unitedhealthgroup.comPosition: Sr Director, Decision Analytics

Dynamic Likelihood-free Inference via Ratio Estimation (DIRE) Parametric statistical models that are implicitly defined in terms of a stochastic data generating process are used in a wide range of scientific disciplines because they enable accurate modeling. However, learning the parameters from observed data is generally very difficult because their likelihood function is typically intractable. Likelihood-free Bayesian inference methods have been proposed which include the frameworks of approximate Bayesian computation (ABC), synthetic likelihood, and its recent generalization that performs likelihood-free inference by ratio estimation (LFIRE). A major difficulty in all these methods is choosing summary statistics that reduce the dimensionality of the data to facilitate inference. While several methods for choosing summary statistics have been proposed for ABC, the literature for synthetic likelihood and LFIRE is very thin to date. We here address this gap in the literature, focusing on the important special case of time-series models. We show that convolutional neural networks trained to predict the input parameters from the data provide suitable summary statistics for LFIRE. On a wide range of time-series models, a single neural network architecture produced equally or more accurate posteriors than alternative methods. …