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1. Predicting Fairness of ML Software Configuration

2. Worst-Case Convergence Time of ML Algorithms via Extreme Value Theory

3. On the Potential and Limitations of Few-Shot In-Context Learning to Generate Metamorphic Specifications for Tax Preparation Software

4. Metamorphic Testing and Debugging of Tax Preparation Software

6. FairLay-ML: Intuitive Remedies for Unfairness in Data-Driven Social-Critical Algorithms

7. Over-Measurement Paradox: Suspension of Thermonuclear Research Center and Need to Update Standards

9. Metamorphic Testing and Debugging of Tax Preparation Software

10. QFuzz: Quantitative Fuzzing for Side Channels

11. Detecting and Understanding Real-World Differential Performance Bugs in Machine Learning Libraries

12. Efficient Detection and Quantification of Timing Leaks with Neural Networks

13. Quantitative Mitigation of Timing Side Channels

14. Data-Driven Debugging for Functional Side Channels

16. Differential Performance Debugging with Discriminant Regression Trees

17. Discriminating Traces with Time


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