Training generative neural networks via Maximum Mean Discrepancy optimization. - Gintare Karolina Dziugaite; Daniel M. Roy 001; Zoubin Ghahramani (UAI, 2015)

Neural Network Matrix Factorization. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (CoRR, 2015)

A study of the effect of JPG compression on adversarial images. - Gintare Karolina Dziugaite; Zoubin Ghahramani; Daniel M. Roy 001 (CoRR, 2016)

Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (UAI, 2017)

Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (CoRR, 2017)

Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (ICML, 2018)

Data-dependent PAC-Bayes priors via differential privacy. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (NeurIPS, 2018)

Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. - Jeffrey Negrea; Mahdi Haghifam; Gintare Karolina Dziugaite; Ashish Khisti; Daniel M. Roy 001 (NeurIPS, 2019)

The Lottery Ticket Hypothesis at Scale. - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (CoRR, 2019)

Revisiting generalization for deep learning: PAC-Bayes, flat minima, and generative models. - Gintare Karolina Dziugaite (2019)

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

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

Linear Mode Connectivity and the Lottery Ticket Hypothesis. - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (ICML, 2020)

In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors. - Jeffrey Negrea; Gintare Karolina Dziugaite; Daniel M. Roy 001 (ICML, 2020)

In search of robust measures of generalization. - Gintare Karolina Dziugaite; Alexandre Drouin; Brady Neal; Nitarshan Rajkumar; Ethan Caballero; Linbo Wang 003; Ioannis Mitliagkas; Daniel M. Roy 001 (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 001; Surya Ganguli (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 001; Gintare Karolina Dziugaite (NeurIPS, 2020)

Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. - Yiding Jiang; Parth Natekar; Manik Sharma; Sumukh K. Aithal; Dhruva Kashyap; Natarajan Subramanyam; Carlos Lassance; Daniel M. Roy 001; Gintare Karolina Dziugaite; Suriya Gunasekar; Isabelle Guyon; Pierre Foret; et al. (NeurIPS (Competition and Demos), 2020)

RelatIF: Identifying Explanatory Training Examples via Relative Influence. - Elnaz Barshan; Marc-Etienne Brunet; Gintare Karolina Dziugaite (CoRR, 2020)

On the role of data in PAC-Bayes bounds. - Gintare Karolina Dziugaite; Kyle Hsu; Waseem Gharbieh; Daniel M. Roy 001 (CoRR, 2020)

Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability. - Gintare Karolina Dziugaite; Shai Ben-David; Daniel M. Roy 001 (CoRR, 2020)

On the Information Complexity of Proper Learners for VC Classes in the Realizable Case. - Mahdi Haghifam; Gintare Karolina Dziugaite; Shay Moran; Daniel M. Roy 001 (CoRR, 2020)

NeurIPS 2020 Competition: Predicting Generalization in Deep Learning. - Yiding Jiang; Pierre Foret; Scott Yak; Daniel M. Roy 001; Hossein Mobahi; Gintare Karolina Dziugaite; Samy Bengio; Suriya Gunasekar; Isabelle Guyon; Behnam Neyshabur (CoRR, 2020)

On the role of data in PAC-Bayes. - Gintare Karolina Dziugaite; Kyle Hsu; Waseem Gharbieh; Gabriel Arpino; Daniel M. Roy 001 (AISTATS, 2021)

Pruning Neural Networks at Initialization: Why Are We Missing the Mark? - Jonathan Frankle; Gintare Karolina Dziugaite; Daniel M. Roy 001; Michael Carbin (ICLR, 2021)

Towards a Unified Information-Theoretic Framework for Generalization. - Mahdi Haghifam; Gintare Karolina Dziugaite; Shay Moran; Daniel M. Roy 001 (NeurIPS, 2021)

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

Probabilistic fine-tuning of pruning masks and PAC-Bayes self-bounded learning. - Soufiane Hayou; Bobby He; Gintare Karolina Dziugaite (CoRR, 2021)

Understanding Generalization via Leave-One-Out Conditional Mutual Information. - Mahdi Haghifam; Shay Moran; Daniel M. Roy 001; Gintare Karolina Dziugaite (ISIT, 2022)

Pruning's Effect on Generalization Through the Lens of Training and Regularization. - Tian Jin; Michael Carbin; Daniel M. Roy 001; Jonathan Frankle; Gintare Karolina Dziugaite (NeurIPS, 2022)

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

The Effect of Data Dimensionality on Neural Network Prunability. - Zachary Ankner; Alex Renda; Gintare Karolina Dziugaite; Jonathan Frankle; Tian Jin (CoRR, 2022)

Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. - Mahdi Haghifam; Borja Rodríguez Gálvez; Ragnar Thobaben; Mikael Skoglund; Daniel M. Roy 001; Gintare Karolina Dziugaite (ALT, 2023)

Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? - Mansheej Paul; Feng Chen 046; Brett W. Larsen; Jonathan Frankle; Surya Ganguli; Gintare Karolina Dziugaite (ICLR, 2023)

Preface of UniReps: the First Workshop on Unifying Representations in Neural Models. - Marco Fumero; Emanuele Rodolà; Clémentine C. J. Dominé; Francesco Locatello; Karolina Dziugaite; Mathilde Caron (UniReps, 2023)

The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning. - Tian Jin; Nolan Clement; Xin Dong; Vaishnavh Nagarajan; Michael Carbin; Jonathan Ragan-Kelley; Gintare Karolina Dziugaite (CoRR, 2023)

Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias. - Yu Yang 007; Eric Gan; Gintare Karolina Dziugaite; Baharan Mirzasoleiman (AISTATS, 2024)

Jaxpruner: A Concise Library for Sparsity Research. - Joo Hyung Lee; Wonpyo Park; Nicole Mitchell; Jonathan Pilault; Johan Samir Obando Ceron; Han-Byul Kim; Namhoon Lee; Elias Frantar; Yun Long; Amir Yazdanbakhsh; Woohyun Han; Shivani Agrawal; et al. (CPAL, 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 (ICLR, 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 001 (ICML, 2024)

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

Simultaneous Linear Connectivity of Neural Networks Modulo Permutation. - Ekansh Sharma; Devin Kwok; Tom Denton; Daniel M. Roy 001; David Rolnick; Gintare Karolina Dziugaite (ECML/PKDD (7), 2024)

Mixture of Experts in a Mixture of RL settings. - Timon Willi; Johan S. Obando-Ceron; Jakob Nicolaus Foerster; Gintare Karolina Dziugaite; Pablo Samuel Castro (RLJ, 2024)

Dataset Difficulty and the Role of Inductive Bias. - Devin Kwok; Nikhil Anand; Jonathan Frankle; Gintare Karolina Dziugaite; David Rolnick (CoRR, 2024)

Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization. - Idan Attias; Gintare Karolina Dziugaite; Mahdi Haghifam; Roi Livni; Daniel M. Roy 001 (CoRR, 2024)

Evaluating Interventional Reasoning Capabilities of Large Language Models. - Tejas Kasetty; Divyat Mahajan; Gintare Karolina Dziugaite; Alexandre Drouin; Dhanya Sridhar (CoRR, 2024)

Unmasking Efficiency: Learning Salient Sparse Models in Non-IID Federated Learning. - Riyasat Ohib; Bishal Thapaliya; Gintare Karolina Dziugaite; Jingyu Liu 001; Vince D. Calhoun; Sergey M. Plis (CoRR, 2024)

Data Selection for Transfer Unlearning. - Nazanin Mohammadi Sepahvand; Vincent Dumoulin; Eleni Triantafillou; Gintare Karolina Dziugaite (CoRR, 2024)

Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. - Eleni Triantafillou; Peter Kairouz; Fabian Pedregosa; Jamie Hayes; Meghdad Kurmanji; Kairan Zhao; Vincent Dumoulin; Júlio C. S. Jacques Júnior; Ioannis Mitliagkas; Jun Wan 001; Lisheng Sun-Hosoya; Sergio Escalera; et al. (CoRR, 2024)

The Non-Local Model Merging Problem: Permutation Symmetries and Variance Collapse. - Ekansh Sharma; Daniel M. Roy 001; Gintare Karolina Dziugaite (CoRR, 2024)

Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization. - Phillip Guo; Aaquib Syed; Abhay Sheshadri; Aidan Ewart; Gintare Karolina Dziugaite (CoRR, 2024)

Unlearning in- vs. out-of-distribution data in LLMs under gradient-based method. - Teodora Baluta; Pascal Lamblin; Daniel Tarlow; Fabian Pedregosa; Gintare Karolina Dziugaite (CoRR, 2024)

Improved Localized Machine Unlearning Through the Lens of Memorization. - Reihaneh Torkzadehmahani; Reza Nasirigerdeh; Georgios Kaissis; Daniel Rueckert; Gintare Karolina Dziugaite; Eleni Triantafillou (CoRR, 2024)

Torque-Aware Momentum. - Pranshu Malviya; Gonçalo Mordido; Aristide Baratin; Reza Babanezhad Harikandeh; Gintare Karolina Dziugaite; Razvan Pascanu; Sarath Chandar (CoRR, 2024)

Leveraging Function Space Aggregation for Federated Learning at Scale. - Nikita Dhawan; Nicole Mitchell; Zachary Charles; Zachary Garrett; Gintare Karolina Dziugaite (Trans. Mach. Learn. Res., 2024)

Linear Weight Interpolation Leads to Transient Performance Gains. - Gaurav Iyer; Gintare Karolina Dziugaite; David Rolnick (Trans. Mach. Learn. Res., 2024)

The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws. - Gintare Karolina Dziugaite; Daniel M. Roy 001 (AISTATS, 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 (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 001; Gintare Karolina Dziugaite (ICLR, 2025)

Soup to go: mitigating forgetting during continual learning with model averaging. - Anat Kleiman; Gintare Karolina Dziugaite; Jonathan Frankle; Sham M. Kakade; Mansheej Paul (CoRR, 2025)

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 001 (CoRR, 2025)

Leveraging Per-Instance Privacy for Machine Unlearning. - Nazanin Mohammadi Sepahvand; Anvith Thudi; Berivan Isik; Ashmita Bhattacharyya; Nicolas Papernot; Eleni Triantafillou; Daniel M. Roy 001; Gintare Karolina Dziugaite (CoRR, 2025)

From Dormant to Deleted: Tamper-Resistant Unlearning Through Weight-Space Regularization. - Shoaib Ahmed Siddiqui; Adrian Weller; David Krueger 001; Gintare Karolina Dziugaite; Michael Curtis Mozer; Eleni Triantafillou (CoRR, 2025)

Continual Learning in Vision-Language Models via Aligned Model Merging. - Ghada Sokar; Gintare Karolina Dziugaite; Anurag Arnab; Ahmet Iscen; Pablo Samuel Castro; Cordelia Schmid (CoRR, 2025)

Less is More: Undertraining Experts Improves Model Upcycling. - Stefan Horoi; Guy Wolf; Eugene Belilovsky; Gintare Karolina Dziugaite (CoRR, 2025)