ArXiv TLDR

Murat Bilgehan Ertan

2 papers · Latest:

Machine Learning

PACZero: PAC-Private Fine-Tuning of Language Models via Sign Quantization

PACZero introduces a novel PAC-private zeroth-order method for fine-tuning LLMs, achieving strong privacy ($I=0$) with usable utility via sign quantization.

2605.06505
Machine Learning

Trade-off Functions for DP-SGD with Subsampling based on Random Shuffling: Tight Upper and Lower Bounds

This paper provides a tight, transparent analysis of the privacy-utility trade-off for DP-SGD using random shuffling subsampling.

2605.06259

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