Dziugaite, Gintare Karolina

44 publications

TMLR 2026 SSFL: Discovering Sparse Unified Subnetworks at Initialization for Efficient Federated Learning Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite, Jingyu Liu, Vince D. Calhoun, Sergey Plis

NeurIPS 2025 From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization Shoaib Ahmed Siddiqui, Adrian Weller, David Krueger, Gintare Karolina Dziugaite, Michael Curtis Mozer, Eleni Triantafillou

TMLR 2025 Improved Localized Machine Unlearning Through the Lens of Memorization Reihaneh Torkzadehmahani, Reza Nasirigerdeh, Georgios Kaissis, Daniel Rueckert, Gintare Karolina Dziugaite, Eleni Triantafillou

ICML 2025 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

ICML 2025 Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization Phillip Huang Guo, Aaquib Syed, Abhay Sheshadri, Aidan Ewart, Gintare Karolina Dziugaite

NeurIPS 2025 On Traceability in $\ell_p$ Stochastic Convex Optimization Sasha Voitovych, Mahdi Haghifam, Idan Attias, Gintare Karolina Dziugaite, Roi Livni, Daniel M. Roy

ICLR 2025 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

ICLR 2025 The Journey Matters: Average Parameter Count over Pre-Training Unifies Sparse and Dense Scaling Laws Tian Jin, Ahmed Imtiaz Humayun, Utku Evci, Suvinay Subramanian, Amir Yazdanbakhsh, Dan Alistarh, Gintare Karolina Dziugaite

AISTATS 2025 The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws Gintare Karolina Dziugaite, Daniel M. Roy

NeurIPSW 2024 Evaluating Interventional Reasoning Capabilities of Large Language Models Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite, Alexandre Drouin, Dhanya Sridhar

ICML 2024 Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy

CPAL 2024 Jaxpruner: A Concise Library for Sparsity Research Joo Hyung Lee, Wonpyo Park, Nicole Elyse Mitchell, Jonathan Pilault, Johan Samir Obando Ceron, Han-Byul Kim, Namhoon Lee, Elias Frantar, Yun Long, Amir Yazdanbakhsh, Woohyun Han, Shivani Agrawal, Suvinay Subramanian, Xin Wang, Sheng-Chun Kao, Xingyao Zhang, Trevor Gale, Aart J.C. Bik, Milen Ferev, Zhonglin Han, Hong-Seok Kim, Yann Dauphin, Gintare Karolina Dziugaite, Pablo Samuel Castro, Utku Evci

TMLR 2024 Leveraging Function Space Aggregation for Federated Learning at Scale Nikita Dhawan, Nicole Elyse Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite

TMLR 2024 Linear Weight Interpolation Leads to Transient Performance Gains Gaurav Iyer, Gintare Karolina Dziugaite, David Rolnick

ICMLW 2024 Linear Weight Interpolation Leads to Transient Performance Gains Gaurav Iyer, Gintare Karolina Dziugaite, David Rolnick

ICML 2024 Mixtures of Experts Unlock Parameter Scaling for Deep RL Johan Samir Obando Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob Nicolaus Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro

ICMLW 2024 Robust Knowledge Unlearning via Mechanistic Localizations Phillip Huang Guo, Aaquib Syed, Abhay Sheshadri, Aidan Ewart, Gintare Karolina Dziugaite

ICMLW 2024 Robust Unlearning via Mechanistic Localizations Phillip Huang Guo, Aaquib Syed, Abhay Sheshadri, Aidan Ewart, Gintare Karolina Dziugaite

ECML-PKDD 2024 Simultaneous Linear Connectivity of Neural Networks Modulo Permutation Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite

ICLR 2024 The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory Before In-Context Learning Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite

NeurIPSW 2024 Unlearning In- vs. Out-of-Distribution Data in LLMs Under Gradient-Based Methods Teodora Baluta, Pascal Lamblin, Daniel Tarlow, Fabian Pedregosa, Gintare Karolina Dziugaite

ICLR 2023 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

NeurIPS 2022 Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks Mansheej Paul, Brett Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite

ICMLW 2022 Pre-Training on a Data Diet: Identifying Sufficient Examples for Early Training Mansheej Paul, Brett W Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite

NeurIPS 2022 Pruning’s Effect on Generalization Through the Lens of Training and Regularization Tian Jin, Michael Carbin, Dan Roy, Jonathan Frankle, Gintare Karolina Dziugaite

NeurIPSW 2022 The Effect of Data Dimensionality on Neural Network Prunability Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite, Jonathan Frankle, Tian Jin

NeurIPSW 2022 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

NeurIPS 2021 Deep Learning on a Data Diet: Finding Important Examples Early in Training Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite

COLT 2021 Information-Theoretic Generalization Bounds for Stochastic Gradient Descent Gergely Neu, Gintare Karolina Dziugaite, Mahdi Haghifam, Daniel M. Roy

AISTATS 2021 On the Role of Data in PAC-Bayes Bounds Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel Roy

ICLR 2021 Pruning Neural Networks at Initialization: Why Are We Missing the Mark? Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin

NeurIPS 2021 Towards a Unified Information-Theoretic Framework for Generalization Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Dan Roy

NeurIPS 2020 Deep Learning Versus Kernel Learning: An Empirical Study of Loss Landscape Geometry and the Time Evolution of the Neural Tangent Kernel Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli

ICML 2020 In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel Roy

NeurIPS 2020 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

ICML 2020 Linear Mode Connectivity and the Lottery Ticket Hypothesis Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin

AISTATS 2020 RelatIF: Identifying Explanatory Training Samples via Relative Influence Elnaz Barshan, Marc-Etienne Brunet, Gintare Karolina Dziugaite

NeurIPS 2020 Sharpened Generalization Bounds Based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite

AISTATS 2020 Stochastic Neural Network with Kronecker Flow Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron Courville

NeurIPS 2019 Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy

NeurIPS 2018 Data-Dependent PAC-Bayes Priors via Differential Privacy Gintare Karolina Dziugaite, Daniel M. Roy

ICML 2018 Entropy-SGD Optimizes the Prior of a PAC-Bayes Bound: Generalization Properties of Entropy-SGD and Data-Dependent Priors Gintare Karolina Dziugaite, Daniel Roy

UAI 2017 Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data Gintare Karolina Dziugaite, Daniel M. Roy

UAI 2015 Training Generative Neural Networks via Maximum Mean Discrepancy Optimization Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani