158 citations · 528 across the 70 of their papers we have counts for
13 papers · 1 filter
Machine Unlearning Doesn't Do What You Think: Lessons for Generative AI Policy and Research
A. Feder Cooper, Christopher A. Choquette-Choo, Miranda Bogen +34
"Machine unlearning" is a popular proposed solution for mitigating the existence of content in an AI model that is problematic for legal or moral reasons, including privacy, copyri…
On the Privacy Risk of In-context Learning
Haonan Duan, Adam Dziedzic, Mohammad Yaghini +2
Large language models (LLMs) are excellent few-shot learners. They can perform a wide variety of tasks purely based on natural language prompts provided to them. These prompts cont…
Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses
David Glukhov, Ziwen Han, Ilia Shumailov +2
Vulnerability of Frontier language models to misuse and jailbreaks has prompted the development of safety measures like filters and alignment training in an effort to ensure safety…
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…
Tighter Privacy Auditing of DP-SGD in the Hidden State Threat Model
Tudor Cebere, Aurélien Bellet, Nicolas Papernot
Machine learning models can be trained with formal privacy guarantees via differentially private optimizers such as DP-SGD. In this work, we focus on a threat model where the adver…
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…
Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy
Jamie Hayes, Ilia Shumailov, Eleni Triantafillou +2
The high cost of model training makes it increasingly desirable to develop techniques for unlearning. These techniques seek to remove the influence of a training example without ha…
Architectural Neural Backdoors from First Principles
Harry Langford, Ilia Shumailov, Yiren Zhao +2
While previous research backdoored neural networks by changing their parameters, recent work uncovered a more insidious threat: backdoors embedded within the definition of the netw…
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…
Fast Exact Unlearning for In-Context Learning Data for LLMs
Andrei I. Muresanu, Anvith Thudi, Michael R. Zhang +1
Modern machine learning models are expensive to train, and there is a growing concern about the challenge of retroactively removing specific training data. Achieving exact unlearni…
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…
Memorization in Self-Supervised Learning Improves Downstream Generalization
Wenhao Wang, Muhammad Ahmad Kaleem, Adam Dziedzic +3
Self-supervised learning (SSL) has recently received significant attention due to its ability to train high-performance encoders purely on unlabeled data-often scraped from the int…