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cs.LG2024
Low-rank finetuning for LLMs: A fairness perspective
Saswat Das, Marco Romanelli, Cuong Tran +3
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. This pa…
cs.LG2024
Disparate Impact on Group Accuracy of Linearization for Private Inference
Saswat Das, Marco Romanelli, Ferdinando Fioretto
Ensuring privacy-preserving inference on cryptographically secure data is a well-known computational challenge. To alleviate the bottleneck of costly cryptographic computations in…