outlier – Andrew Fairless, Ph.D.

What I Read: Density Kernel Depth for Outlier Detection

By Andrew Fairless on January 24, 2024November 20, 2023

https://www.kdnuggets.com/density-kernel-depth-for-outlier-detection-in-functional-data Density Kernel Depth for Outlier Detection in Functional DataKulbir SinghNovember 8, 2023 “The Density Kernel Depth (DKD) method provides a nuanced approach to detect outliers in functional data…”

What I Read: Federated, Protects Privacy

By Andrew Fairless on January 12, 2023December 4, 2022

https://pair.withgoogle.com/explorables/federated-learning/ How Federated Learning Protects Privacy “With federated learning, it’s possible to collaboratively train a model with data from multiple users without any raw data leaving their devices.”

What I Read: introduction to NumPyro

By Andrew Fairless on September 19, 2022August 22, 2022

https://dfm.io/posts/intro-to-numpyro/ An astronomer’s introduction to NumPyroDan Foreman-MackeyJul 28 2022 “In this post… I’ll focus primarily on providing an introduction to NumPyro, which is a probabilistic programming library that provides anContinue readingWhat I Read: introduction to NumPyro

What I Read: Anomaly Detection Metrics

By Andrew Fairless on August 25, 2022July 18, 2022

https://medium.com/@katser/a-review-of-anomaly-detection-metrics-with-a-lot-of-related-information-736d88774712 A Review of Anomaly Detection MetricsIurii KatserJul 12 “Anomaly detection… is a problem… to identify unusual patterns that do not conform to expected behavior…. we will talk about anomalyContinue readingWhat I Read: Anomaly Detection Metrics

What I Read: ML model drift in production

By Andrew Fairless on March 21, 2022January 20, 2022

https://towardsdatascience.com/my-data-drifted-whats-next-how-to-handle-ml-model-drift-in-production-78719ef007b1 “My data drifted. What’s next?” How to handle ML model drift in production.An introductory overview of the possible steps.Elena SamuylovaDec 1, 2021 ““I have a model in production, andContinue readingWhat I Read: ML model drift in production

What I Read: Isolation Forest

By Andrew Fairless on February 16, 2021January 18, 2021

https://towardsdatascience.com/isolation-forest-is-the-best-anomaly-detection-algorithm-for-big-data-right-now-e1a18ec0f94f Isolation Forest is the best Anomaly Detection Algorithm for Big Data Right NowAndrew YoungNov 13 “Isolation forest or “iForest” is an astoundingly beautiful and elegantly simple algorithm that identifiesContinue readingWhat I Read: Isolation Forest

Tag: outlier