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Starts 2 June 2025 14:32

Ends 2 June 2025

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Know When You Know: Handling Adversarial Data by Abstaining

Explore sequential prediction in stochastic settings with adversarial interference. Learn strategies for handling distribution shifts and making confident predictions while abstaining from uncertain cases.
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Simons Institute

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Overview

Explore sequential prediction in stochastic settings with adversarial interference. Learn strategies for handling distribution shifts and making confident predictions while abstaining from uncertain cases.

Syllabus

  • Introduction to Sequential Prediction
  • Basics of sequential prediction models
    Stochastic settings overview
  • Understanding Adversarial Interference
  • Types and sources of adversarial interference
    Impact of adversarial settings on predictions
  • Handling Distribution Shifts
  • Identifying distribution shifts
    Strategies to adapt to distribution shifts
  • Confident Predictions with Abstention
  • Concept of abstaining in uncertain scenarios
    Metrics for confidence estimation
    Techniques to integrate abstention in prediction models
  • Strategies for Robust Sequential Prediction
  • Defensive modeling approaches
    Incorporating redundancy and diversity
  • Evaluating Model Performance with Adversarial Data
  • Benchmarking techniques
    Case studies and real-world examples
  • Advanced Topics
  • Theoretical foundations of adversarial robustness
    Recent advancements in adversarial training
  • Practical Applications and Tools
  • Common tools and libraries for handling adversarial data
    Building and testing models with abstention mechanisms
  • Conclusion and Future Directions
  • Emerging trends in adversarial data handling
    Ethical considerations in abstaining from uncertain predictions

Subjects

Data Science