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Intro
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Learning and Learnability One of the goals of theory of ML
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Learnability for Today's World
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Learnability Q1. What concepts can be learned in presence of strategic and adversarial behavior? → Lessons for todays world from decade of efforts for understanding
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Tutorial Overview
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Stochastic (Offline) Settings Usage Example: Learning to detect natural phenomenon or fixed distribution objects, eg, trees, animals, etc.
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Formal Setup: Stochastic setting
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Alternative Setup: (Stochastic) Offline Learning
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What characterizes offline learnability?
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VC Dimension Example
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Why VC Dimension?
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Stochastic (Offline) Settings Usage Examples Controlling the content quality, face adversarial manipulation of future instances and have to updated
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Formal Setup: Online vs Stochastic Setting
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Characterizing Online Learnability Role of VC dimension - Finite VC dimension is not sufficient, because of thresholds on a line. • VC dimension focuses on labeling a set . But we need to consider la…
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Characterization of Online Learnability
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Algorithms based on Littlestone Dimension
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Solution Concepts
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore the fundamentals of learning theory and incentives in this comprehensive lecture from the Learning and Games Boot Camp. Delve into the concept of learnability in the presence of strategic and adversarial behavior, drawing insights from a decade of research. Examine stochastic (offline) and online learning settings, understanding their formal setups and real-world applications. Investigate the role of VC dimension in characterizing learnability, and discover why it may not be sufficient in online scenarios. Analyze the Littlestone dimension and its importance in online learning algorithms. Gain valuable knowledge on solution concepts and their applications in modern machine learning challenges.

Learning and Incentives

Simons Institute
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