Portrait of Gintare Karolina Dziugaite

Gintare Karolina Dziugaite

Associate Industry Member
Adjunct Professor, McGill University, School of Computer Science
Senior Research Scientist, Google DeepMind
Research Topics
Deep Learning
Information Theory
Machine Learning Theory
Website
Google Scholar
LinkedIn

Biography

Gintare Karolina Dziugaite is a senior research scientist at Google DeepMind in Toronto, and an adjunct professor at the McGill University School of Computer Science. Prior to joining Google, she led the Trustworthy AI program at Element AI (ServiceNow). Her research combines theoretical and empirical approaches to understanding deep learning.

Dziugaite is well known for her work on network and data sparsity, developing algorithms and uncovering effects on generalization and other metrics. She pioneered the study of linear mode connectivity, first connecting it to the existence of lottery tickets, then to loss landscapes and the mechanism of iterative magnitude pruning. Another major focus of her research is understanding generalization in deep learning and, more generally, the development of information-theoretic methods for studying generalization. Her most recent work looks at removing the influence of data on the model (unlearning).

Dziugaite obtained her PhD in machine learning from the University of Cambridge under the supervision of Zoubin Ghahramani. Prior to that, she studied mathematics at the University of Warwick and read Part III in Mathematics at the University of Cambridge, receiving a Master of Advanced Study (MASt) in mathematics. She has participated in a number of long-term programs at the Institute for Advanced Study in Princeton, NJ, and at the Simons Institute for the Theory of Computing at the University of Berkeley.

Publications

Continual Learning in Vision-Language Models via Aligned Model Merging
Ghada Sokar
Gintare Karolina Dziugaite
Anurag Arnab
Ahmet Iscen
Pablo Samuel Castro
Cordelia Schmid
Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors pl… (see more)asticity over the stability needed to retain prior knowledge. While existing approaches attempt to mitigate catastrophic forgetting, a bias towards recent tasks persists as they build upon this sequential nature. In this work we present a new perspective based on model merging to maintain stability while still retaining plasticity. Rather than just sequentially updating the model weights, we propose merging newly trained task parameters with previously learned ones, promoting a better balance. To maximize the effectiveness of the merging process, we propose a simple mechanism that promotes learning aligned weights with previous ones, thereby avoiding interference when merging. We evaluate this approach on large Vision-Language Models (VLMs), and demonstrate its effectiveness in reducing forgetting, increasing robustness to various task orders and similarities, and improving generalization.
2025-05-30
ArXiv (preprint)
arxiv.org
arxiv.org
Continual Learning in Vision-Language Models via Aligned Model Merging
Ghada Sokar
Gintare Karolina Dziugaite
Anurag Arnab
Ahmet Iscen
Pablo Samuel Castro
Cordelia Schmid
Continual learning is conventionally tackled through sequential fine-tuning, a process that, while enabling adaptation, inherently favors pl… (see more)asticity over the stability needed to retain prior knowledge. While existing approaches attempt to mitigate catastrophic forgetting, a bias towards recent tasks persists as they build upon this sequential nature. In this work we present a new perspective based on model merging to maintain stability while still retaining plasticity. Rather than just sequentially updating the model weights, we propose merging newly trained task parameters with previously learned ones, promoting a better balance. To maximize the effectiveness of the merging process, we propose a simple mechanism that promotes learning aligned weights with previous ones, thereby avoiding interference when merging. We evaluate this approach on large Vision-Language Models (VLMs), and demonstrate its effectiveness in reducing forgetting, increasing robustness to various task orders and similarities, and improving generalization.
2025-05-30
ArXiv (preprint)
arxiv.org
arxiv.org
From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization
Shoaib Ahmed Siddiqui
Adrian Weller
David Krueger 0001
Gintare Karolina Dziugaite
M. C. Mozer
Eleni Triantafillou
Recent unlearning methods for LLMs are vulnerable to relearning attacks: knowledge believed-to-be-unlearned re-emerges by fine-tuning on a s… (see more)mall set of (even seemingly-unrelated) examples. We study this phenomenon in a controlled setting for example-level unlearning in vision classifiers. We make the surprising discovery that forget-set accuracy can recover from around 50% post-unlearning to nearly 100% with fine-tuning on just the retain set -- i.e., zero examples of the forget set. We observe this effect across a wide variety of unlearning methods, whereas for a model retrained from scratch excluding the forget set (gold standard), the accuracy remains at 50%. We observe that resistance to relearning attacks can be predicted by weight-space properties, specifically,
2025-05-28
ArXiv (preprint)
arxiv.org
arxiv.org
From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization
Shoaib Ahmed Siddiqui
Adrian Weller
David Krueger 0001
Gintare Karolina Dziugaite
M. C. Mozer
Eleni Triantafillou
Recent unlearning methods for LLMs are vulnerable to relearning attacks: knowledge believed-to-be-unlearned re-emerges by fine-tuning on a s… (see more)mall set of (even seemingly-unrelated) examples. We study this phenomenon in a controlled setting for example-level unlearning in vision classifiers. We make the surprising discovery that forget-set accuracy can recover from around 50% post-unlearning to nearly 100% with fine-tuning on just the retain set -- i.e., zero examples of the forget set. We observe this effect across a wide variety of unlearning methods, whereas for a model retrained from scratch excluding the forget set (gold standard), the accuracy remains at 50%. We observe that resistance to relearning attacks can be predicted by weight-space properties, specifically,
2025-05-28
ArXiv (preprint)
arxiv.org
arxiv.org
Leveraging Per-Instance Privacy for Machine Unlearning
Nazanin Mohammadi Sepahvand
Anvith Thudi
Berivan Isik
Ashmita Bhattacharyya
Nicolas Papernot
Eleni Triantafillou
Daniel M. Roy
Gintare Karolina Dziugaite
2025-05-01
ICML.cc/2025/Conference (poster)
doi.org
openreview.net
Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization
Phillip Huang Guo
Aaquib Syed
Abhay Sheshadri
Aidan Ewart
Gintare Karolina Dziugaite
2025-05-01
ICML.cc/2025/Conference (poster)
doi.org
openreview.net
On the Dichotomy Between Privacy and Traceability in ℓp Stochastic Convex Optimization
Sasha Voitovych
MAHDI HAGHIFAM
Idan Attias
Gintare Karolina Dziugaite
Roi Livni
Daniel M. Roy
2025-02-24
ArXiv (preprint)
doi.org
arxiv.org
On the Dichotomy Between Privacy and Traceability in $\ell_p$ Stochastic Convex Optimization
Sasha Voitovych
MAHDI HAGHIFAM
Idan Attias
Gintare Karolina Dziugaite
Roi Livni
Daniel M. Roy
In this paper, we investigate the necessity of memorization in stochastic convex optimization (SCO) under …
2025-02-24
ArXiv (preprint)
doi.org
arxiv.org
On the Dichotomy Between Privacy and Traceability in ℓp Stochastic Convex Optimization
Sasha Voitovych
MAHDI HAGHIFAM
Idan Attias
Gintare Karolina Dziugaite
Roi Livni
Daniel M. Roy
2025-02-24
ArXiv (preprint)
doi.org
arxiv.org
On the Dichotomy Between Privacy and Traceability in $\ell_p$ Stochastic Convex Optimization
Sasha Voitovych
MAHDI HAGHIFAM
Idan Attias
Gintare Karolina Dziugaite
Roi Livni
Daniel M. Roy
In this paper, we investigate the necessity of memorization in stochastic convex optimization (SCO) under …
2025-02-24
ArXiv (preprint)
arxiv.org
arxiv.org
On Traceability in $\ell_p$ Stochastic Convex Optimization
Sasha Voitovych
MAHDI HAGHIFAM
Idan Attias
Gintare Karolina Dziugaite
Roi Livni
Daniel M. Roy
In this paper, we investigate the necessity of traceability for accurate learning in stochastic convex optimization (SCO) under …
2025-02-24
ArXiv (preprint)
arxiv.org
arxiv.org
Selective Unlearning via Representation Erasure Using Domain Adversarial Training
Nazanin Mohammadi Sepahvand
Eleni Triantafillou
Hugo Larochelle
Doina Precup
James J. Clark
Daniel M. Roy
Gintare Karolina Dziugaite
When deploying machine learning models in the real world, we often face the challenge of “unlearning” specific data points or subsets a… (see more)fter training. Inspired by Domain-Adversarial Training of Neural Networks (DANN), we propose a novel algorithm,SURE, for targeted unlearning.SURE treats the process as a domain adaptation problem, where the “forget set” (data to be removed) and a validation set from the same distribution form two distinct domains. We train a domain classifier to discriminate between representations from the forget and validation sets.Using a gradient reversal strategy similar to DANN, we perform gradient updates to the representations to “fool” the domain classifier and thus obfuscate representations belonging to the forget set. Simultaneously, gradient descent is applied to the retain set (original training data minus the forget set) to preserve its classification performance. Unlike other unlearning approaches whose training objectives are built based on model outputs, SURE directly manipulates the representations.This is key to ensure robustness against a set of more powerful attacks than currently considered in the literature, that aim to detect which examples were unlearned through access to learned embeddings. Our thorough experiments reveal that SURE has a better unlearning quality to utility trade-off compared to other standard unlearning techniques for deep neural networks.
2025-01-22
ICLR.cc/2025/Conference (poster)
openreview.net
openreview.net