Gintare Karolina Dziugaite

Associate Industry Member

Adjunct Professor, McGill University, School of Computer Science

Senior Research Scientist, Google DeepMind

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

When Majorities Prevent Learning: Eliminating Bias to Improve Worst-group and Out-of-distribution Generalization

Yu Yang

Gintare Karolina Dziugaite

Baharan Mirzasoleiman

Modern neural networks trained on large datasets have achieved state-of-the-art (in-distribution) generalization performance on various task… (see more)s. However, their good generalization performance has been shown to be contributed largely to overfitting spurious biases in large datasets. This is evident by the poor generalization performance of such models on minorities and out-of-distribution data. To alleviate this issue, subsampling the majority groups has been shown to be very effective. However, it is not clear how to find the subgroups (e.g. within a class) in large real-world datasets. Besides, naively subsampling the majority groups can entirely deplete some of their smaller sub-populations and drastically harm the in-distribution performance. Here, we show that tracking gradient trajectories of examples in initial epochs allows for finding large subpopulations of data points. We leverage this observation and propose an importance sampling method that is biased towards selecting smaller subpopulations, and eliminates bias in the large subpopulations. Our experiments confirm the effectiveness of our approach in eliminating spurious biases and learning higher-quality models with superior in- and out-of-distribution performance on various datasets.

2023-02-01

ICLR.cc/2023/Conference (rejected)

openreview.net

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.

2020-06-03

Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics (published)

proceedings.mlr.press

arxiv.org

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.

arxiv.org

Stochastic Neural Network with Kronecker Flow

Chin-Wei Huang

Ahmed Touati

Pascal Vincent

Gintare Karolina Dziugaite

Alexandre Lacoste

Aaron Courville

2019-06-10

ArXiv (preprint)

arxiv.org