Видео ютуба по тегу Pac Learning

Extending Learnability to Auxiliary-Input Cryptographic Primitives and Meta-PAC Learning
Extending Learnability to Auxiliary-Input Cryptographic Primitives and Meta-PAC Learning
Week 12: Machine Learning Deep Dive | NPTEL noc24-cs101 Full Discussion
Week 12: Machine Learning Deep Dive | NPTEL noc24-cs101 Full Discussion
A (condensed) primer on PAC-Bayesian learning, followed by News from the PAC-Bayes frontline
A (condensed) primer on PAC-Bayesian learning, followed by News from the PAC-Bayes frontline
L4 PAC Learning (1) - Algorithms in Machine Learning: Guarantees and Analyses
L4 PAC Learning (1) - Algorithms in Machine Learning: Guarantees and Analyses
Classical PAC learning | L15 Advanced Topics in Quantum Information Theory 2022
Classical PAC learning | L15 Advanced Topics in Quantum Information Theory 2022
Gal Vardi - A Theory of Learning with Autoregressive Chain of Thought (Heb)
Gal Vardi - A Theory of Learning with Autoregressive Chain of Thought (Heb)
Louis Schatzki - Quantum Statistical Query Learning II of II - IPAM at UCLA
Louis Schatzki - Quantum Statistical Query Learning II of II - IPAM at UCLA
PAC Learning Explained: Computational Learning Theory for Beginners
PAC Learning Explained: Computational Learning Theory for Beginners
理解机器学习 01 - PAC学习框架
理解机器学习 01 - PAC学习框架
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks
Algorithms and SQ Lower Bounds for PAC Learning One-Hidden-Layer ReLU Networks
Machine Learning - Lecture 14(Fall 2016)
Machine Learning - Lecture 14(Fall 2016)
The Roots of AI: PAC Learning Theory (1984)
The Roots of AI: PAC Learning Theory (1984)
Shang-Hua Teng: Optimization, Machine Learning, and Game Theory: From the Lens of Smoothed Analysis
Shang-Hua Teng: Optimization, Machine Learning, and Game Theory: From the Lens of Smoothed Analysis
Part 1: generalization and PAC bayesian learning
Part 1: generalization and PAC bayesian learning
Statistical Learning Theory 2025: Class 13
Statistical Learning Theory 2025: Class 13
Computational Learning Theory by Tom Mitchell
Computational Learning Theory by Tom Mitchell
continuation of part 1 : generalization and PAC learning
continuation of part 1 : generalization and PAC learning
Vojtěch Havlíček - Quantum Statistical Query Learning I of II - IPAM at UCLA
Vojtěch Havlíček - Quantum Statistical Query Learning I of II - IPAM at UCLA
CS8850: Learning via Uniform Convergence
CS8850: Learning via Uniform Convergence
PAC Learning Social Networks via Threshold-Based Opinion Dyanamics
PAC Learning Social Networks via Threshold-Based Opinion Dyanamics
10-601 Machine Learning Spring 2015 - Recitation 9
10-601 Machine Learning Spring 2015 - Recitation 9
2 pac class, Aaron X. Smith, Race Rhetoric and Revolution
2 pac class, Aaron X. Smith, Race Rhetoric and Revolution
STATS 231C: Theories of Machine Learning -- Spring 22 -- Lecture 6
STATS 231C: Theories of Machine Learning -- Spring 22 -- Lecture 6
Learning Reliable Rules under Class Imbalance
Learning Reliable Rules under Class Imbalance
Welcome to the PAC Teaching & Learning Center
Welcome to the PAC Teaching & Learning Center