Abstract: As an applied statistician you get in touch with many challenging problems in need of a statistical solution. Often, your client/colleague already has a working solution and just wants to clarify a small statistical detail with you. Equally often, your intuition suggests you that the working solution is not statistically adequate, but how to substantiate this? As motivating example we use the statistical process control methodology used in Sarma et al. (2018) for monitoring a binomial proportion as part of a syndromic surveillance kit.

Federated Learning is a technology that allows you to build machine learning systems when your datacenter can’t get direct access to model training data. The data remains in its original location, which helps to ensure privacy and reduces communication costs.

At: Bank of CanadaLocation: Ottawa, Canada Web: www.bankofcanada.caPosition: Data Scientist

PC-LPGM Biological processes underlying the basic functions of a cell involve complex interactions between genes. From a technical point of view, these interactions can be represented through a graph where genes and their connections are, respectively, nodes and edges. The main objective of this paper is to develop a statistical framework for modelling the interactions between genes when the activity of genes is measured on a discrete scale. In detail, we define a new algorithm for learning the structure of undirected graphs, PC-LPGM, proving its theoretical consistence in the limit of infinite observations. The proposed algorithm shows promising results when applied to simulated data as well as to real data. …

Patrick Atwater writes:

Last month, experts from the AI world came together for the Artificial Intelligence Conference at San Francisco to discuss insights, opportunities, challenges, and trends related to the rapidly expanding field of AI. The conference included hands-on trainings, tutorials, startup showcase (which was won by Clobotics), keynotes, sessions, expo, and social events.

A small blog post with a riddle, simulation, theory and a concluding rhyme.

The resurrection of neural networks as a technique has helped propel the field of machine learning to the forefront of commercial applications. Today’s most popular applications focus on finding patterns in data and exploiting those patterns for very narrow tasks. But what if we want more from machine learning? Instead of trying to contort the methods we have today to achieve marginal gains in generalizability, what if we took another angle altogether?