113 citations · 147 across the 19 of their papers we have counts for
7 papers · 1 filter
Entropy-Aware On-Policy Distillation of Language Models
Woogyeol Jin, Taywon Min, Yongjin Yang +5
On-policy distillation is a promising approach for transferring knowledge between language models, where a student learns from dense token-level signals along its own trajectories.…
Evaluating the Dynamics of Membership Privacy in Deep Learning
Yuetian Chen, Zhiqi Wang, Nathalie Baracaldo +2
Membership inference attacks (MIAs) pose a critical threat to the privacy of training data in deep learning. Despite significant progress in attack methodologies, our understanding…
Membership Inference Attacks as Privacy Tools: Reliability, Disparity and Ensemble
Zhiqi Wang, Chengyu Zhang, Yuetian Chen +3
Membership inference attacks (MIAs) pose a significant threat to the privacy of machine learning models and are widely used as tools for privacy assessment, auditing, and machine u…
Split, Unlearn, Merge: Leveraging Data Attributes for More Effective Unlearning in LLMs
Swanand Ravindra Kadhe, Farhan Ahmed, Dennis Wei +2
Large language models (LLMs) have shown to pose social and ethical risks such as generating toxic language or facilitating malicious use of hazardous knowledge. Machine unlearning…
FairSISA: Ensemble Post-Processing to Improve Fairness of Unlearning in LLMs
Swanand Ravindra Kadhe, Anisa Halimi, Ambrish Rawat +1
Training large language models (LLMs) is a costly endeavour in terms of time and computational resources. The large amount of training data used during the unsupervised pre-trainin…
LESS-VFL: Communication-Efficient Feature Selection for Vertical Federated Learning
Timothy Castiglia, Yi Zhou, Shiqiang Wang +3
We propose LESS-VFL, a communication-efficient feature selection method for distributed systems with vertically partitioned data. We consider a system of a server and several parti…