Know When You Know: Handling Adversarial Data by Abstaining

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

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Subjects

Data Science