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