725 citations · 912 across the 20 of their papers we have counts for
21 papers
You Don't Need All That Attention: Surgical Memorization Mitigation in Text-to-Image Diffusion Models
Kairan Zhao, Eleni Triantafillou, Peter Triantafillou
Generative models have been shown to "memorize" certain training data, leading to verbatim or near-verbatim generating images, which may cause privacy concerns or copyright infring…
Leveraging Per-Instance Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand, Anvith Thudi, Berivan Isik +5
We present a principled, per-instance approach to quantifying the difficulty of unlearning via fine-tuning. We begin by sharpening an analysis of noisy gradient descent for unlearn…
From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization
Shoaib Ahmed Siddiqui, Adrian Weller, David Krueger +3
Recent unlearning methods for LLMs are vulnerable to relearning attacks: knowledge believed-to-be-unlearned re-emerges by fine-tuning on a small set of (even seemingly-unrelated) e…
Redirection for Erasing Memory (REM): Towards a universal unlearning method for corrupted data
Stefan Schoepf, Michael Curtis Mozer, Nicole Elyse Mitchell +4
Machine unlearning is studied for a multitude of tasks, but specialization of unlearning methods to particular tasks has made their systematic comparison challenging. To address th…
Improved Localized Machine Unlearning Through the Lens of Memorization
Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis +3
Machine unlearning refers to removing the influence of a specified subset of training data from a machine learning model, efficiently, after it has already been trained. This is im…
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…
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…
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition
Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa +12
We present the findings of the first NeurIPS competition on unlearning, which sought to stimulate the development of novel algorithms and initiate discussions on formal and robust…
What makes unlearning hard and what to do about it
Kairan Zhao, Meghdad Kurmanji, George-Octavian Bărbulescu +2
Machine unlearning is the problem of removing the effect of a subset of training data (the ''forget set'') from a trained model without damaging the model's utility e.g. to comply…
Data Selection for Transfer Unlearning
Nazanin Mohammadi Sepahvand, Vincent Dumoulin, Eleni Triantafillou +1
As deep learning models are becoming larger and data-hungrier, there are growing ethical, legal and technical concerns over use of data: in practice, agreements on data use may cha…
Leveraging tropical reef, bird and unrelated sounds for superior transfer learning in marine bioacoustics
Ben Williams, Bart van Merriënboer, Vincent Dumoulin +12
Machine learning has the potential to revolutionize passive acoustic monitoring (PAM) for ecological assessments. However, high annotation and compute costs limit the field's effic…
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…
BIRB: A Generalization Benchmark for Information Retrieval in Bioacoustics
Jenny Hamer, Eleni Triantafillou, Bart van Merriënboer +4
The ability for a machine learning model to cope with differences in training and deployment conditions--e.g. in the presence of distribution shift or the generalization to new cla…
Machine Unlearning in Learned Databases: An Experimental Analysis
Meghdad Kurmanji, Eleni Triantafillou, Peter Triantafillou
Machine learning models based on neural networks (NNs) are enjoying ever-increasing attention in the DB community. However, an important issue has been largely overlooked, namely t…
Towards Unbounded Machine Unlearning
Meghdad Kurmanji, Peter Triantafillou, Jamie Hayes +1
Deep machine unlearning is the problem of `removing' from a trained neural network a subset of its training set. This problem is very timely and has many applications, including th…
In Search for a Generalizable Method for Source Free Domain Adaptation
Malik Boudiaf, Tom Denton, Bart van Merriënboer +2
Source-free domain adaptation (SFDA) is compelling because it allows adapting an off-the-shelf model to a new domain using only unlabelled data. In this work, we apply existing SFD…
Learning a Universal Template for Few-shot Dataset Generalization
Eleni Triantafillou, Hugo Larochelle, Richard Zemel +1
Few-shot dataset generalization is a challenging variant of the well-studied few-shot classification problem where a diverse training set of several datasets is given, for the purp…
Probing Few-Shot Generalization with Attributes
Mengye Ren, Eleni Triantafillou, Kuan-Chieh Wang +5
Despite impressive progress in deep learning, generalizing far beyond the training distribution is an important open challenge. In this work, we consider few-shot classification, a…
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples
Eleni Triantafillou, Tyler Zhu, Vincent Dumoulin +8
Few-shot classification refers to learning a classifier for new classes given only a few examples. While a plethora of models have emerged to tackle it, we find the procedure and d…
Meta-Learning for Semi-Supervised Few-Shot Classification
Mengye Ren, Eleni Triantafillou, Sachin Ravi +5
In few-shot classification, we are interested in learning algorithms that train a classifier from only a handful of labeled examples. Recent progress in few-shot classification has…
Few-Shot Learning Through an Information Retrieval Lens
Eleni Triantafillou, Richard Zemel, Raquel Urtasun
Few-shot learning refers to understanding new concepts from only a few examples. We propose an information retrieval-inspired approach for this problem that is motivated by the inc…