Search Results for author:
Found 6 papers, 2 papers with code
R.R.: Unveiling LLM Training Privacy through Recollection and Ranking
In the second stage, we design a new criterion to score each PII candidate and rank them.
ScaleOT: Privacy-utility-scalable Offsite-tuning with Dynamic LayerReplace and Selective Rank Compression
Offsite-tuning is a privacy-preserving method for tuning large language models (LLMs) by sharing a lossy compressed emulator from the LLM owners with data owners for downstream task tuning.
Fine-Tuning Gemma-7B for Enhanced Sentiment Analysis of Financial News Headlines
In this study, we explore the application of sentiment analysis on financial news headlines to understand investor sentiment.
Forgetting Fast in Recommender Systems
In this paper, we investigate fast machine unlearning techniques for recommender systems that can remove the effect of a small amount of training data from the recommendation model without incurring the full cost of retraining.
Obtaining Dyadic Fairness by Optimal Transport
Fairness has been taken as a critical metric in machine learning models, which is considered as an important component of trustworthy machine learning.
Fair Differential Privacy Can Mitigate the Disparate Impact on Model Accuracy
This paper proposes a fair differential privacy algorithm (FairDP) to mitigate the disparate impact on model accuracy for each class.
