February 2026

Releasing data once and for all under noninteractive Local Differential Privacy (LDP) enables complete data reusability, but the resulting noise may create bias in subsequent analyses. In this work, we leverage the Weierstrass transform to characterize this bias in binary classification. We prove that inverting this transform leads to a bias-correction method to compute unbiased estimates of nonlinear functions on examples released under LDP. We then build a novel stochastic gradient descent algorithm called Inverse Weierstrass Private SGD […]

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Automated feature engineering (AFE) enables AI systems to autonomously construct high-utility representations from raw tabular data. However, existing AFE methods rely on statistical heuristics, yielding brittle features that fail under distribution shift. We introduce CAFE, a framework that reformulates AFE as a causally-guided sequential decision process, bridging causal discovery with reinforcement learning-driven feature construction. Phase I learns a sparse directed acyclic graph over features and the target to obtain soft causal priors, grouping features as direct, indirect, or […]

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In this quiz, you’ll test your understanding of how to install Python. This quiz covers questions about how to check which version of Python is installed on your machine, and how you can install or update Python on various operating systems. [ Improve Your Python With 🐍 Python Tricks 💌 – Get a short & sweet Python Trick delivered to your inbox every couple of days. >> Click here to learn more and see examples ]

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:::info Astounding Stories of Super-Science February, 2026, by Astounding Stories is part of HackerNoon’s Book Blog Post series. You can jump to any chapter in this book here. The Moors and the Fens, volume 1 (of 3) – Chapter V: Renders a Change of Abode necessary. Astounding Stories of Super-Science February 2026: The Moors and the Fens, volume 1 (of 3) – Chapter V Renders a Change of Abode necessary By J. H. Riddell ::: Death and poverty—perpetual travellers, […]

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In an era defined by AI-driven efficiency and hyper-automation, a paradoxical situation lies at the heart of corporate IT. Modern software development relies on automated CI/CD pipelines, yet mission-critical migration projects are often executed with the digital equivalent of chisels and hammers. While headlines tout the latest AI advancements, many enterprises embarking on data modernization are stuck in the past. They rely on slow, expensive, and risk-prone manual methods that closely resemble those used in IT projects from […]

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In the age of digital epidemiology, epidemiologists are faced by an increasing amount of data of growing complexity and dimensionality. Machine learning is a set of powerful tools that can help to analyze such enormous amounts of data. This chapter lays the methodological foundations for successfully applying machine learning in epidemiology. It covers the principles of supervised and unsupervised learning and discusses the most important machine learning methods. Strategies for model evaluation and hyperparameter optimization are developed and […]

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Counterfactual explanations are increasingly vital for understanding and trusting machine learning models. This study presents, Desirability Rating based Counterfactual (DeRaC), a generalized framework for generating valid counterfactual explanations applicable to multi-dimensional classification problems, including single and multi-output classification with binary and multi-label outputs. By expanding the definition of counterfactual validity through a novel “desirability rating,” the approach addresses limitations in existing methods for complex output spaces. This work details a novel framework, introducing concepts like partially valid counterfactuals […]

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