Learning Probability Distributions: What Can and Can't Be Done
Explore statistical learning, focusing on guaranteed generalizations from finite samples and characterizing learnable distribution families. Gain insights into modern paradigms in generalization.
Inverse Results for Isoperimetric Inequalities - Lecture 4
Explore inverse isoperimetric problems in graphs, focusing on the Fourier-Entropy-Influence conjecture and related inequalities. Progress and applications in PAC learning discussed.
Inverse Results for Isoperimetric Inequalities - Lecture 3
Explore inverse isoperimetric problems in graphs, focusing on the Fourier-Entropy-Influence conjecture and related inequalities. Progress and applications discussed.
Inverse Results for Isoperimetric Inequalities - Part II
Explores inverse isoperimetric problems in graphs, focusing on the Fourier-Entropy-Influence conjecture and related inequalities, with applications to PAC learning and other areas.
Inverse Results for Isoperimetric Inequalities - Part I
Explore inverse isoperimetric problems in graphs, focusing on the Fourier-Entropy-Influence conjecture and related inequalities. Discover applications to PAC learning and recent progress in the field.
PAC Learning in Machine Learning Theory - Lecture 13
Delve into computational learning theory, exploring the PAC model and Occam's razor theorem for advanced understanding of machine learning fundamentals and theoretical frameworks.
Proper vs Improper Quantum PAC Learning
Explore quantum PAC learning theory through a comparative analysis of proper and improper learning methods, focusing on the Quantum Coupon Collector problem and its padded variant.
Provable Advantage in Quantum PAC Learning
Discover groundbreaking research on quantum PAC learning, exploring provable advantages over classical methods with square root improvements in sample complexity and theoretical quantum information science.
Information-Theoretic Generalization Bounds for Quantum Learning
Explore a unified mathematical framework for quantum learning theory, covering state discrimination, PAC learning, and generalization bounds using quantum mutual information and optimal transport concepts.
PAC Mode Estimation Using PPR Martingale Confidence Sequences
Delve into advanced statistical methods for mode estimation using PPR martingale confidence sequences, with applications in election forecasting and blockchain smart contract verification.
Machine Learning: Understanding VC Dimensions and Computational Learning Theory - Lecture 18
Delve into advanced computational learning theory concepts and VC dimensions, exploring fundamental principles that shape machine learning algorithm capabilities and limitations.
Machine Learning: Shattering and VC Dimension - Lecture 17B
Delve into computational learning theory, focusing on shattering concepts and VC dimension fundamentals in machine learning through theoretical frameworks and practical applications.
Agnostic Learning in Computational Learning Theory - Lecture 17
Delve into computational learning theory and explore advanced concepts of agnostic learning, focusing on theoretical frameworks and practical applications in machine learning.
Machine Learning: Positive and Negative Learnability Results - Lecture 16
Explore key theoretical foundations of machine learning through positive and negative learnability results, enhancing your understanding of learning theory fundamentals.
PAC Learnability in Deep Learning - Lecture 20
Delve into statistical machine learning fundamentals, exploring PAC learnability concepts and their application in deep learning, with focus on generalization theories and practical challenges.
PAC Learning of Decision Trees
Explore the theoretical foundations of PAC learning algorithms and their application to decision tree construction in this advanced computational complexity seminar.
PAC Learning: From Finite Families to CNF Formulas - Lecture on March 20, 2023
Dive into PAC learning concepts, exploring finite families, conjunctions, k-CNFs, and NP-completeness in proper learning for 2-term CNF through advanced computational complexity theory.
Introduction to Probably Approximately Correct (PAC) Learning
Dive into computational complexity and PAC-learning concepts through an in-depth seminar discussion at the prestigious Kolmogorov seminar series on complexity theory.
Computable PAC Learning - Part 2
Dive into advanced concepts of computable PAC learning through an in-depth exploration of theoretical frameworks and computational complexity in machine learning theory.
Computable PAC Learning - Part 1
Dive into computational complexity and PAC learning theory through an in-depth exploration of foundational concepts, statistical learning frameworks, and algorithmic approaches.
PAC Learning in Machine Learning Theory - Lecture 13
Explore the fundamental concepts of PAC (Probably Approximately Correct) learning theory and its applications in theoretical machine learning frameworks.