- robustness
- privacy
- guide
- competition
- paper
- exploration
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Reassessing EMNLP 2024’s Best Paper: Does Divergence-Based Calibration for Membership Inference Attacks Hold Up?
TL;DR: No.
A critical analysis of the EMNLP Best Paper proposing a divergence-based calibration for Membership Inference Attacks (MIAs). We explore its experimental shortcomings, issues with temporally shifted benchmarks, and what this means for machine learning awards. -
My submission to the ETI Challenge
Description of my entry to the ETI (Erasing the Invisible) challenge (co-located with NeurIPS) for watermark-removal.
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My submission to the TDC Trojan Detection Challenge
Description of my entry to the TDC Trojan Detection challenge (co-located with NeurIPS 2023).
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My submission to the MICO Challenge
Description of my entry to the MICO challenge (co-located with SaTML) for membership inference that won me the 2nd place on the CIFAR track.
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Dissecting Distribution Inference
Describing our work on distribution inference attacks.
