Teaching Machines That the World Keeps Changing: Supervised Learning with Evolving Tasks and Performance Guarantees | AI Trend Blend AITrendBlend Machine Learning Math About Continual Learning · Journal of Machine Learning Research 26 (2025) 1–59 · BCAM · University of the Basque Country · 22 min read Teaching Machines That the World Keeps Changing: One […]
An Axiomatic Definition of Hierarchical Clustering: Why Hartigan Was Right All Along | AI Trend Blend AITrendBlend Machine Learning Math Computer Vision About Statistical Learning Theory · Journal of Machine Learning Research 26 (2025) 1–26 · 18 min read Three Rules That Define What a Cluster Actually Is: The Axiomatic Case for Hartigan’s Cluster Tree
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Bayes Meets Bernstein: Why Meta-Learning Finally Gets Fast — A PAC-Bayes Breakthrough | AI Trend Blend AITrendBlend Machine Learning Math About Machine Learning Theory · Journal of Machine Learning Research 26 (2025) · University of Tokyo, ESSEC Business School & CNRS / Sorbonne Université · 16 min read Why Meta-Learning Suddenly Gets Smarter the More
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Claude Code for Medical Research Pipelines (2026 Guide) — 10 Prompts for Clinical AI Engineering Claude Code · Medical AI · Clinical Data Engineering · 2026 Guide Claude Code for Medical Research Pipelines: 10 Prompts for Clinical AI Engineering (2026) Claude Code Medical Research Clinical Pipelines HIPAA De-identification Survival Analysis Clinical NLP EHR Engineering Regulatory
Escaping Saddle Points in Bilevel Optimization — A Breakthrough in Machine Learning Theory | AI Trend Blend AITrendBlend Machine Learning Math About Machine Learning Theory · Journal of Machine Learning Research 26 (2025) · UC Davis, University at Buffalo & Rice University · 18 min read Why Your Machine Learning Model Gets Stuck — And
Liquid Biopsy and AI: Multi-Cancer Early Detection — 10 Claude Code Prompts (2026 Guide) AITrendBlend Prompts Machine Learning Claude About Medical AI · Liquid Biopsy · Genomics · 2026 Guide Liquid Biopsy and AI: Multi-Cancer Early Detection — 10 Claude Code Prompts for MCED Research (2026) Liquid Biopsy Multi-Cancer Detection cfDNA Analysis Methylation Classifiers Fragmentomics
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TTGDA: Two-Timescale Gradient Descent Ascent for Nonconvex Minimax Optimization | AI Trend Blend Optimization Theory · Journal of Machine Learning Research 26 (2025) 1–45 · 19 min read The Two Clocks That Fixed GAN Training: A Complete Theory of Two-Timescale Gradient Descent Ascent Tianyi Lin, Chi Jin, and Michael I. Jordan from Columbia, Princeton, and
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How to Tune a Robust Regression Model Without Knowing the Noise: Adaptive Error Estimation for Unregularized M-Estimators | AI Trend Blend AITrendBlend Machine Learning Computer Vision About High-Dimensional Statistics · Journal of Machine Learning Research 26 (2025) 1–40 · Rutgers University · University of Chicago · 18 min read You Can Tune a Robust Regression
Test-Time Training on Video Streams: Why Forgetting Is Actually a Feature | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Computer Vision · Journal of Machine Learning Research 26 (2025) 1–29 · UC Berkeley · Stanford · Meta AI · UC San Diego · 20 min read Why Your Model Should Forget Yesterday’s Frames:
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Dist-SI: Selective Inference with Distributed Data via Randomized Lasso | AI Trend Blend AITrendBlend Machine Learning Computer Vision About Statistical Inference · Journal of Machine Learning Research 26 (2025) 1–44 · 20 min read How Dist-SI Lets Hospitals Run Joint Studies Without Sharing Patient Records — Selective Inference Across Distributed Data Sifan Liu (Stanford) and
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