density – Andrew Fairless, Ph.D.

What I Read: Gaussians

By Andrew Fairless on February 13, 2025October 25, 2024

https://gestalt.ink/gaussians Understanding Gaussians “The Gaussian distribution, or normal distribution is a key subject in statistics, machine learning, physics, and pretty much any other field that deals with data and probability.”

What I Read: Bounded Kernel Density Estimation

By Andrew Fairless on February 4, 2025October 25, 2024

https://towardsdatascience.com/bounded-kernel-density-estimation-2082dff3f47f Bounded Kernel Density EstimationThomas RouchFeb 28, 2024 “…when it comes to estimating continuous densities, people often resort to treating it as a mysterious black box. However, understanding this conceptContinue readingWhat I Read: Bounded Kernel Density Estimation

What I Read: KL All You Need

By Andrew Fairless on August 21, 2024June 4, 2024

https://blog.alexalemi.com/kl-is-all-you-need.html KL is All You NeedAlexander A. Alemi2024-01-08 “…the core of essentially all modern machine learning methods is a single universal objective: Kullback-Leibler (KL) divergence minimization…. Understand KL, understand theContinue readingWhat I Read: KL All You Need

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: Concept Drift Without Labeled Data

By Andrew Fairless on September 26, 2022August 22, 2022

https://concept-drift.fastforwardlabs.com/ Inferring Concept Drift Without Labeled DataFF22Aug 2021 “…we explore broadly applicable approaches for dealing with concept drift when labeled data is not readily accessible. We’ll start by defining whatContinue readingWhat I Read: Concept Drift Without Labeled Data

What I Read: learning-to-rank

By Andrew Fairless on October 6, 2021September 21, 2021

https://www.amazon.science/blog/using-learning-to-rank-to-precisely-locate-where-to-deliver-packages Using learning-to-rank to precisely locate where to deliver packagesModels adapted from information retrieval deal well with noisy GPS input and can leverage map information.By George FormanSeptember 15, 2021 “ForContinue readingWhat I Read: learning-to-rank

What I Read: Flows for simultaneous manifold learning and density estimation

By Andrew Fairless on December 31, 2020December 20, 2020

https://arxiv.org/abs/2003.13913 Flows for simultaneous manifold learning and density estimationJohann Brehmer, Kyle Cranmer “We introduce manifold-learning flows (M-flows), a new class of generative models that simultaneously learn the data manifold asContinue readingWhat I Read: Flows for simultaneous manifold learning and density estimation

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