193 citations · 535 across the 23 of their papers we have counts for
13 papers · 1 filter
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
Ilia Shumailov, Jamie Hayes, Eleni Triantafillou +6
Exact unlearning was first introduced as a privacy mechanism that allowed a user to retract their data from machine learning models on request. Shortly after, inexact schemes were…
LLM Dataset Inference: Did you train on my dataset?
Pratyush Maini, Hengrui Jia, Nicolas Papernot +1
The proliferation of large language models (LLMs) in the real world has come with a rise in copyright cases against companies for training their models on unlicensed data from the…
Fairness Feedback Loops: Training on Synthetic Data Amplifies Bias
Sierra Wyllie, Ilia Shumailov, Nicolas Papernot
Model-induced distribution shifts (MIDS) occur as previous model outputs pollute new model training sets over generations of models. This is known as model collapse in the case of…
Regulation Games for Trustworthy Machine Learning
Mohammad Yaghini, Patty Liu, Franziska Boenisch +1
Existing work on trustworthy machine learning (ML) often concentrates on individual aspects of trust, such as fairness or privacy. Additionally, many techniques overlook the distin…
Decentralised, Collaborative, and Privacy-preserving Machine Learning for Multi-Hospital Data
Congyu Fang, Adam Dziedzic, Lin Zhang +5
Machine Learning (ML) has demonstrated its great potential on medical data analysis. Large datasets collected from diverse sources and settings are essential for ML models in healt…
Robust and Actively Secure Serverless Collaborative Learning
Olive Franzese, Adam Dziedzic, Christopher A. Choquette-Choo +7
Collaborative machine learning (ML) is widely used to enable institutions to learn better models from distributed data. While collaborative approaches to learning intuitively prote…