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Low-rank finetuning for LLMs: A fairness perspective
Low-rank approximation techniques have become the de facto standard for fine-tuning Large Language Models (LLMs) due to their reduced computational and memory requirements.
Disparate Impact on Group Accuracy of Linearization for Private Inference
Ensuring privacy-preserving inference on cryptographically secure data is a well-known computational challenge.
Privacy and Bias Analysis of Disclosure Avoidance Systems
The results show that, contrary to popular beliefs, traditional differential privacy techniques may be superior in terms of accuracy and fairness to differential private counterparts of widely used DA mechanisms.
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