- DeepMind Relies on this Old Statistical Method to Build Fair Machine Learning Models - Oct 23, 2020.
Causal Bayesian Networks are used to model the influence of fairness attributes in a dataset.
- DeepMind is Using This Old Technique to Evaluate Fairness in Machine Learning Models - Oct 28, 2019.
Visualizing the datasets is an essential component to identify potential sources of bias and unfairness. DeepMind relied on a method called Causal Bayesian networks (CBNs) to represent and estimate unfairness in a dataset.
- The Book of Why - Jun 1, 2018.
Judea Pearl has made noteworthy contributions to artificial intelligence, Bayesian networks, and causal analysis. These achievements notwithstanding, Pearl holds some views many statisticians may find odd or exaggerated.
- THE BOOK OF WHY: The New Science of Cause and Effect - May 15, 2018.
A Turing Prize-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize AI.
- What is a Bayesian Neural Network? - Dec 5, 2017.
BNNs are important in specific settings, especially when we care about uncertainty very much.
- Top KDnuggets tweets, Nov 15-21: DeepLearning is “shallow”: here are underlying concepts you need - Nov 27, 2017.
Also: New Poll: Data Science / Machine Learning methods you used; The amazing predictive power of conditional probability in Bayes Nets; The 10 Statistical Techniques Data Scientists Need to Master.
- The amazing predictive power of conditional probability in Bayes Nets - Nov 13, 2017.
This article explains how Bayes Nets gain remarkable predictive power by their use of conditional probability. This adds to several other salient strengths, making them a preeminent method for prediction and understanding variables’ effects.
- How Bayesian Networks Are Superior in Understanding Effects of Variables - Nov 9, 2017.
Bayes Nets have remarkable properties that make them better than many traditional methods in determining variables’ effects. This article explains the principle advantages.
- 5 Tribes of Machine Learning: Nov 24 ACM Webinar with Pedro Domingos, moderated by Gregory Piatetsky - Nov 10, 2015.
Prof. Pedro Domingos, a leading AI/Machine Learning researcher will talk about 5 main schools in machine learning, each with its own master algorithm, a possible universal Master Algorithm, and implications for society. KDnuggets Editor Gregory Piatetsky will moderate.
- BayesiaLab User Conference, Sep 16-24, UCLA - Sep 2, 2014.
Research practitioners from leading organizations will gather at UCLA for the only event dedicated to applied research and analytics with Bayesian networks and BayesiaLab. Pre-conference program includes courses on BayesiaLab and Causal Inference with Graphical Models.
- Globys: Research Scientist – Dynamic Bayesian Networks - Mar 31, 2014.
Developing core science into advanced technical capabilities that work on real-world problems at scale, presenting to stakeholders, and delivering the output to software architects.