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

Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
Mansheej Paul
Feng Chen
Brett W. Larsen
Jonathan Frankle
Surya Ganguli
Gintare Karolina Dziugaite
Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that can s… (see more)till be trained to the same accuracy as the full network (i.e. matching). Iterative magnitude pruning (IMP) is a state of the art algorithm that can find such highly sparse matching subnetworks, known as winning tickets. IMP operates by iterative cycles of training, masking smallest magnitude weights, rewinding back to an early training point, and repeating. Despite its simplicity, the underlying principles for when and how IMP finds winning tickets remain elusive. In particular, what useful information does an IMP mask found at the end of training convey to a rewound network near the beginning of training? How does SGD allow the network to extract this information? And why is iterative pruning needed? We develop answers in terms of the geometry of the error landscape. First, we find that
2023-01-31
ICLR.cc/2023/Conference (notable)
doi.org
openreview.net
Unmasking the Lottery Ticket Hypothesis: Efficient Adaptive Pruning for Finding Winning Tickets
Mansheej Paul
Feng Chen
Brett W. Larsen
Jonathan Frankle
Surya Ganguli
Gintare Karolina Dziugaite
Modern deep learning involves training costly, highly overparameterized networks, thus motivating the search for sparser networks that requi… (see more)re less compute and memory but can still be trained to the same accuracy as the full network (i.e. matching). Iterative magnitude pruning (IMP) is a state of the art algorithm that can find such highly sparse matching subnetworks, known as winning tickets, that can be retrained from initialization or an early training stage. IMP operates by iterative cycles of training, masking a fraction of smallest magnitude weights, rewinding unmasked weights back to an early training point, and repeating. Despite its simplicity, the underlying principles for when and how IMP finds winning tickets remain elusive. In particular, what useful information does an IMP mask found at the end of training convey to a rewound network near the beginning of training? We find that—at higher sparsities—pairs of pruned networks at successive pruning iterations are connected by a linear path with zero error barrier if and only if they are matching. This indicates that masks found at the end of training encodes information about the identity of an axial subspace that intersects a desired linearly connected mode of a matching sublevel set. We leverage this observation to design a simple adaptive pruning heuristic for speeding up the discovery of winning tickets and achieve a 30% reduction in computation time on CIFAR-100. These results make progress toward demystifying the existence of winning tickets with an eye towards enabling the development of more efficient pruning algorithms.
2022-10-19
NeurIPS.cc/2022/Workshop/HITY (accepted)
openreview.net
openreview.net
Understanding Generalization via Leave-One-Out Conditional Mutual Information
MAHDI HAGHIFAM
Shay Moran
Daniel M. Roy
Gintare Karolina Dziugaite
2021-12-31
ISIT (published)
doi.org
arxiv.org
Probabilistic fine-tuning of pruning masks and PAC-Bayes self-bounded learning
Soufiane Hayou
Bo He
Gintare Karolina Dziugaite
2021-10-21
ArXiv (preprint)
arxiv.org
arxiv.org
Stochastic Neural Network with Kronecker Flow
Chin-wei Huang
Ahmed Touati
Pascal Vincent
Gintare Karolina Dziugaite
Alexandre Lacoste
Aaron Courville
Recent advances in variational inference enable the modelling of highly structured joint distributions, but are limited in their capacity to… (see more) scale to the high-dimensional setting of stochastic neural networks. This limitation motivates a need for scalable parameterizations of the noise generation process, in a manner that adequately captures the dependencies among the various parameters. In this work, we address this need and present the Kronecker Flow, a generalization of the Kronecker product to invertible mappings designed for stochastic neural networks. We apply our method to variational Bayesian neural networks on predictive tasks, PAC-Bayes generalization bound estimation, and approximate Thompson sampling in contextual bandits. In all setups, our methods prove to be competitive with existing methods and better than the baselines.
2020-06-02
Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (published)
doi.org
proceedings.mlr.press
In Search of Robust Measures of Generalization
Gintare Karolina Dziugaite
Alexandre Drouin
Brady Neal
Nitarshan Rajkumar
Ethan Caballero
Linbo Wang
Ioannis Mitliagkas
Daniel M. Roy
One of the principal scientific challenges in deep learning is explaining generalization, i.e., why the particular way the community now tra… (see more)ins networks to achieve small training error also leads to small error on held-out data from the same population. It is widely appreciated that some worst-case theories -- such as those based on the VC dimension of the class of predictors induced by modern neural network architectures -- are unable to explain empirical performance. A large volume of work aims to close this gap, primarily by developing bounds on generalization error, optimization error, and excess risk. When evaluated empirically, however, most of these bounds are numerically vacuous. Focusing on generalization bounds, this work addresses the question of how to evaluate such bounds empirically. Jiang et al. (2020) recently described a large-scale empirical study aimed at uncovering potential causal relationships between bounds/measures and generalization. Building on their study, we highlight where their proposed methods can obscure failures and successes of generalization measures in explaining generalization. We argue that generalization measures should instead be evaluated within the framework of distributional robustness.
2019-12-31
NeurIPS (published)
doi.org
arxiv.org