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#PAC Learning

Showing: 31 courses
Simons Institute

Learning Probability Distributions: What Can and Can't Be Done

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Explore statistical learning, focusing on guaranteed generalizations from finite samples and characterizing learnable distribution families. Gain insights into modern paradigms in generalization.
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1 Lesons
1 hour 12 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

Inverse Results for Isoperimetric Inequalities - Lecture 4

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Explore inverse isoperimetric problems in graphs, focusing on the Fourier-Entropy-Influence conjecture and related inequalities. Progress and applications in PAC learning discussed.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

Inverse Results for Isoperimetric Inequalities - Lecture 3

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Explore inverse isoperimetric problems in graphs, focusing on the Fourier-Entropy-Influence conjecture and related inequalities. Progress and applications discussed.
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1 Lesons
1 hour 11 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

Inverse Results for Isoperimetric Inequalities - Part II

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Explores inverse isoperimetric problems in graphs, focusing on the Fourier-Entropy-Influence conjecture and related inequalities, with applications to PAC learning and other areas.
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1 Lesons
1 hour 14 minutes
On-Demand
Free-Video
Hausdorff Center for Mathematics

Inverse Results for Isoperimetric Inequalities - Part I

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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.
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1 Lesons
1 hour 6 minutes
On-Demand
Free-Video
UofU Data Science

PAC Learning in Machine Learning Theory - Lecture 13

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Delve into computational learning theory, exploring the PAC model and Occam's razor theorem for advanced understanding of machine learning fundamentals and theoretical frameworks.
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1 Lesons
1 hour 20 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Proper vs Improper Quantum PAC Learning

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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.
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1 Lesons
20 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Provable Advantage in Quantum PAC Learning

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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.
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1 Lesons
24 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Information-Theoretic Generalization Bounds for Quantum Learning

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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.
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1 Lesons
27 minutes
On-Demand
Free-Video
Centre for Networked Intelligence, IISc

PAC Mode Estimation Using PPR Martingale Confidence Sequences

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Delve into advanced statistical methods for mode estimation using PPR martingale confidence sequences, with applications in election forecasting and blockchain smart contract verification.
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1 Lesons
59 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Understanding VC Dimensions and Computational Learning Theory - Lecture 18

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Delve into advanced computational learning theory concepts and VC dimensions, exploring fundamental principles that shape machine learning algorithm capabilities and limitations.
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1 Lesons
1 hour 4 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Shattering and VC Dimension - Lecture 17B

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Delve into computational learning theory, focusing on shattering concepts and VC dimension fundamentals in machine learning through theoretical frameworks and practical applications.
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1 Lesons
30 minutes
On-Demand
Free-Video
UofU Data Science

Agnostic Learning in Computational Learning Theory - Lecture 17

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Delve into computational learning theory and explore advanced concepts of agnostic learning, focusing on theoretical frameworks and practical applications in machine learning.
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1 Lesons
51 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Positive and Negative Learnability Results - Lecture 16

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Explore key theoretical foundations of machine learning through positive and negative learnability results, enhancing your understanding of learning theory fundamentals.
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1 Lesons
53 minutes
On-Demand
Free-Video
Data Science Courses

PAC Learnability in Deep Learning - Lecture 20

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Delve into statistical machine learning fundamentals, exploring PAC learnability concepts and their application in deep learning, with focus on generalization theories and practical challenges.
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1 Lesons
44 minutes
On-Demand
Free-Video
Kolmogorov-Seminar

PAC Learning of Decision Trees

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Explore the theoretical foundations of PAC learning algorithms and their application to decision tree construction in this advanced computational complexity seminar.
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1 Lesons
2 hours
On-Demand
Free-Video
Kolmogorov-Seminar

PAC Learning: From Finite Families to CNF Formulas - Lecture on March 20, 2023

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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.
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1 Lesons
2 hours 1 minute
On-Demand
Free-Video
Kolmogorov-Seminar

Introduction to Probably Approximately Correct (PAC) Learning

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Dive into computational complexity and PAC-learning concepts through an in-depth seminar discussion at the prestigious Kolmogorov seminar series on complexity theory.
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1 Lesons
1 hour 41 minutes
On-Demand
Free-Video
Kolmogorov-Seminar

Computable PAC Learning - Part 2

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Dive into advanced concepts of computable PAC learning through an in-depth exploration of theoretical frameworks and computational complexity in machine learning theory.
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1 Lesons
1 hour 32 minutes
On-Demand
Free-Video
Kolmogorov-Seminar

Computable PAC Learning - Part 1

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Dive into computational complexity and PAC learning theory through an in-depth exploration of foundational concepts, statistical learning frameworks, and algorithmic approaches.
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1 Lesons
1 hour 25 minutes
On-Demand
Free-Video
UofU Data Science

PAC Learning in Machine Learning Theory - Lecture 13

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Explore the fundamental concepts of PAC (Probably Approximately Correct) learning theory and its applications in theoretical machine learning frameworks.
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1 Lesons
41 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Understanding Hypothesis Spaces Through Shattering - Lecture 17

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Delve into the concept of shattering and its application in analyzing infinite hypothesis spaces within machine learning theory and computational learning frameworks.
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1 Lesons
1 hour 19 minutes
On-Demand
Free-Video
Churchill CompSci Talks

PAC Learning

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Explore PAC learning framework in machine learning, covering supervised classification, learnability criteria, and VC dimension. Understand theoretical foundations and practical implications.
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13 Lesons
31 minutes
On-Demand
Free-Video
UofU Data Science

Positive and Negative Learnability Results in Machine Learning - Lecture 15

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Delve into fundamental learnability concepts in machine learning, exploring both positive and negative results that shape our understanding of learning algorithms and their limitations.
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1 Lesons
18 minutes
On-Demand
Free-Video
UofU Data Science

Data Mining: Choosing k for MinHashing - Spring 2023

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Delve into advanced min hashing concepts, exploring PAC learning, Central Limit Theorem, and Chernoff-Hoeffding Inequality to optimize k-value selection for accurate Jaccard similarity estimates.
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10 Lesons
21 minutes
On-Demand
Free-Video
Squid: Schools for Quantum Information Development

Optimal Lower Bounds for Quantum Learning via Information Theory

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Delve into quantum learning theory's fundamental bounds, exploring information-theoretic approaches to PAC and agnostic models, with insights into the Quantum Coupon Collector problem and state distinguishability.
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10 Lesons
21 minutes
On-Demand
Free-Video
Institute for Advanced Study

Characterizations of PAC Learnability

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Explore key characterizations of PAC learnability in machine learning theory with insights from postdoctoral researcher Nataly Brukhim.
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1 Lesons
15 minutes
On-Demand
Free-Video
Simons Institute

Distribution Learning Meets Graph Structure Sampling

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Explore the connection between PAC-learning of graphical models and efficient graph structure sampling, leveraging online learning algorithms for new insights in high-dimensional distribution learning.
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1 Lesons
27 minutes
On-Demand
Free-Video
UofU Data Science

Machine Learning: Computational Learning Theory and Batch Learning - Lecture 12

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Delve into formal models of learnability, focusing on batch learning concepts, mistake bound models, and PAC learning theory in machine learning applications.
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12 Lesons
48 minutes
On-Demand
Free-Video
Google TechTalks

Tree Learning: Optimal Algorithms and Sample Complexity - Hierarchical Clustering and PAC Learning

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Explore optimal algorithms and sample complexity for learning hierarchical tree representations from labeled data, covering PAC learning, online learning, and efficient tree classifier construction.
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19 Lesons
35 minutes
On-Demand
Free-Video
Institute for Pure & Applied Mathematics (IPAM)

Learning in a Dynamic World: From PAC to Prospective Learning Theory

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Explore dynamic learning theory and its evolution beyond PAC learning, examining how AI systems can adapt to changing data distributions and goals while maintaining performance over time.
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1 Lesons
51 minutes
On-Demand
Free-Video