Machine learning

Prof. John Harlim | Learning solution operator of dynamical systems with diffusion maps kernel ri...

Prof. Gitta Kutyniok | Beyond Scaling: Optimization and Learning for Sustainable AI

Andrew Lee: Decomposing Query-Key Feature Interactions Using Contrastive Covariances (2026-05-13)

Fine-Tuning LLMs for Real-World Tool Calling: Lessons from tau2-bench

Expanding the Capabilities of Tabular Foundation Models

Enhancing Training Data Pipelines with Lance and the Multimodal Lakehouse

Encrypted inferences over decision trees

Emulating Real-World PII with a Large-Scale Synthetic Dataset to Audit LLM Memorization

AI Is Learning at the Wrong Level of Abstraction — Matthieu Wyart

Research Presentation

Research Discussion

Panel Session

MLT | Week-8 | Solve with us | Naive Bayes Algorithm

MLT | Week-8 | Solve with us | Naive Bayes Algorithm

Agents That Learn: Building Your First RL Agent with JAX

A computational phase transition for learning-to-sample from Ising models

Lec 21: Graph Execution & TensorFlow Optimization

Lec 20: Optimization Input Pipelines with tf.data

Lec 19: TensorFlow Execution Models & Performance Basics

How abundant are good interpolators?

What Is AI Model Collapse? Why AI Could Forget Reality

MLT | Week-8 | Naive Bayes Algorithm | Live Session

MLT | Week-8 | Naive Bayes Algorithm | Live Session

Optimal Inference Schedules for Masked Diffusion Models

Claude Skills : Tu peux enfin TOUT automatiser en montrant ton ÉCRAN !

Claude Skills: You can finally automate EVERYTHING by showing your SCREEN!

A new class of algorithms for trajectory inference

Hybridization of Neural Networks and Numerical Solvers in JAX with Differentiable Physics

Stanford CS329A Self-Improving AI Agents | Part 9 | Future Research Areas

Stanford CS329A Self-Improving AI Agents | Part 7 | Self-Improvement and Deep Research Agents

Stanford CS329A Self-Improving AI Agents | Part 6 | Train Time Scaling/Scaling RL

Stanford CS329A Self-Improving AI Agents | Part 5 | Planning and Multi-Step Reasoning

Stanford CS329A Self-Improving AI Agents | Part 4 | Learning from Feedback with Tools/Code

Stanford CS329A Self-Improving AI Agents | Part 3 | Robust Verification

Efficient Reinforcement Learning for Diffusion Models

Architectures for an AI agent for route planning and Next-Latent Prediction

MLT | Week-7 | Solve with us-Decision Tree and K-NN

Stanford CS229 Machine Learning | Spring 2026 | Lecture 9: K-Means and GMM (non-EM)

Stanford CS229 Machine Learning | Spring 2026 | Lecture 20: GMM (EM), PCA

Stanford CS229 Machine Learning | Spring 2026 | Lecture 18: GMM (EM), PCA

Stanford CS229 Machine Learning | Spring 2026 | Lecture 13: LLMs, Next-Word Prediction Loss

Stanford CS229 Machine Learning | Spring 2026 | Lecture 10: GMM (EM), PCA

Stanford CS229 Machine Learning | Spring 2026 | Lecture 6: Dataset Split, ML Advice