variance – Andrew Fairless, Ph.D.

What I Read: Statistical Intuitions

By Andrew Fairless on April 22, 2025January 18, 2025

https://arxiv.org/abs/2409.18842 Classical Statistical (In-Sample) Intuitions Don’t Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random DesignsAlicia Curth27 Sep 2024 “…we show that classical intuitions relatingContinue readingWhat I Read: Statistical Intuitions

What I Read: Regularization, polynomial bases

By Andrew Fairless on November 7, 2024August 25, 2024

https://alexshtf.github.io/2024/06/03/PolynomialBasesRegProps.html Alex ShtoffRegularization properties of polynomial basesJun 3, 2024 “We used the Bernstein basis to demonstrate the importance of chosing a “good” polynomial basis, and that other well-known bases mayContinue readingWhat I Read: Regularization, polynomial bases

What I Read: Double Machine Learning for causal inference

By Andrew Fairless on July 21, 2021July 2, 2021

https://towardsdatascience.com/double-machine-learning-for-causal-inference-78e0c6111f9d Double Machine Learning for causal inferenceHow Double Machine Learning for causal inference works, from the theoretical foundations to an example of application.Borja VelascoJun 25 “This post tries to explain,Continue readingWhat I Read: Double Machine Learning for causal inference

What I Read: Comprehensive Guide to Ensemble Learning

By Andrew Fairless on June 29, 2021June 7, 2021

https://www.kdnuggets.com/2021/05/comprehensive-guide-ensemble-learning.html A Comprehensive Guide to Ensemble Learning – Exactly What You Need to KnowThis article covers ensemble learning methods, and exactly what you need to know in order to understandContinue readingWhat I Read: Comprehensive Guide to Ensemble Learning

What I Read: Rip van Winkle’s Razor, Adaptive Data Analysis

By Andrew Fairless on April 17, 2021April 10, 2021

http://www.offconvex.org/2021/04/07/ripvanwinkle/ Rip van Winkle’s Razor, a Simple New Estimate for Adaptive Data AnalysisSanjeev Arora, Yi ZhangApr 7, 2021 “Can you trust a model whose designer had access to the test/holdoutContinue readingWhat I Read: Rip van Winkle’s Razor, Adaptive Data Analysis

What I Read: Attention with Performers

By Andrew Fairless on February 7, 2021January 11, 2021

https://ai.googleblog.com/2020/10/rethinking-attention-with-performers.html Rethinking Attention with PerformersFriday, October 23, 2020Posted by Krzysztof Choromanski and Lucy Colwell, Research Scientists, Google Research “To resolve these issues, we introduce the Performer, a Transformer architecture withContinue readingWhat I Read: Attention with Performers

What I Read: Reproducing Deep Double Descent

By Andrew Fairless on February 7, 2021January 11, 2021

https://hippocampus-garden.com/double_descent/ Reproducing Deep Double DescentShion HondaJune 13, 2020 “…you’ve probably heard of bias-variance trade-off… But, do you know that there is a continuation of the U-shaped test risk curve? Surprisingly,Continue readingWhat I Read: Reproducing Deep Double Descent

What I Read: Deep Double Descent: Where Bigger Models and More Data Hurt

By Andrew Fairless on February 6, 2021January 11, 2021

https://arxiv.org/abs/1912.02292 Deep Double Descent: Where Bigger Models and More Data HurtPreetum Nakkiran, Gal Kaplun, Yamini Bansal, Tristan Yang, Boaz Barak, Ilya Sutskever “We show that a variety of modern deepContinue readingWhat I Read: Deep Double Descent: Where Bigger Models and More Data Hurt

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