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How Mathematical Discoveries are Made

@machinelearnbot

In one of my previous articles, you can learn the process about how discoveries are made by research scientists, from exploratory analysis, testing, simulations, data science guesswork, all the way to the discovery of a new theory and state-of-the-art statistical modeling,including new, fundamental mathematical/statistical equations.


How Mathematical Discoveries are Made

@machinelearnbot

In one of my previous articles, you can learn the process about how discoveries are made by research scientists, from stating the problem, exploratory analysis, testing, simulations, data science guesswork, all the way to the discovery of a new theory and state-of-the-art statistical modeling,including new, fundamental mathematical/statistical equations.


Local Causal Discovery of Direct Causes and Effects

Neural Information Processing Systems

We focus on the discovery and identification of direct causes and effects of a target variable in a causal network. State-of-the-art algorithms generally need to find the global causal structures in the form of complete partial directed acyclic graphs in order to identify the direct causes and effects of a target variable. While these algorithms are effective, it is often unnecessary and wasteful to find the global structures when we are only interested in one target variable (such as class labels). We propose a new local causal discovery algorithm, called Causal Markov Blanket (CMB), to identify the direct causes and effects of a target variable based on Markov Blanket Discovery. CMB is designed to conduct causal discovery among multiple variables, but focuses only on finding causal relationships between a specific target variable and other variables.


Empirical Discovery in Linguistics

AAAI Conferences

A discovery system for detecting correspondences in data is described, based on the familiar induction methods of J. S. Mill. Given a set of observations, the system induces the "causally" related facts in these observations. Its application to empirical linguistic discovery is described. The paper is organized as follows. I begin the discussion by revealing two developments, the transformationalists' critique of "discovery procedures" and naive inductivism, which have led to the neglect of discovery issues, arguing that more attention needs to be paid to discovery in linguistics.


Opensource & Machine Learning for GDPR Data Discovery

#artificialintelligence

GDPR (EU General Data Protection Regulation) is around the corner and bigger companies are getting ready to adopt it as they already know what kind of penalties come from non-compliance. It replaces replaces the Data Protection Directive 95/46/EC and was designed to harmonize data privacy laws across Europe and it is the biggest change on data privacy regulation in 20 years for Europe. While GDPR main elements can be a little tricky to understand, one thing is clear as sensitive Data Discovery is mandatory, so you can find the Personal and sensitive information on your data repositories, that can be almost everything from databases to files. Basically, we focus our data discovery on three main areas: column discovery, data discovery and file discovery. Column discovery is easy to understand, based on specific keywords or sentences we find column names on databases and match it with possible sensitive data.