Gintare Karolina Dziugaite

Publication Activity (10 Years)

Years Active: 2015-2024
Publications (10 Years): 61

Top Topics

Generalization Bounds

Conditional Mutual Information

Top Venues

Publications

Nazanin Mohammadi Sepahvand, Vincent Dumoulin, Eleni Triantafillou, Gintare Karolina Dziugaite
Data Selection for Transfer Unlearning. CoRR (2024)
Tejas Kasetty, Divyat Mahajan, Gintare Karolina Dziugaite, Alexandre Drouin, Dhanya Sridhar
Evaluating Interventional Reasoning Capabilities of Large Language Models. CoRR (2024)
Riyasat Ohib, Bishal Thapaliya, Gintare Karolina Dziugaite, Jingyu Liu, Vince D. Calhoun, Sergey M. Plis
Unmasking Efficiency: Learning Salient Sparse Models in Non-IID Federated Learning. CoRR (2024)
Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning. ICLR (2024)
Ekansh Sharma, Devin Kwok, Tom Denton, Daniel M. Roy, David Rolnick, Gintare Karolina Dziugaite
Simultaneous linear connectivity of neural networks modulo permutation. CoRR (2024)
Eleni Triantafillou, Peter Kairouz, Fabian Pedregosa, Jamie Hayes, Meghdad Kurmanji, Kairan Zhao, Vincent Dumoulin, Júlio C. S. Jacques Júnior, Ioannis Mitliagkas, Jun Wan, Lisheng Sun-Hosoya, Sergio Escalera, Gintare Karolina Dziugaite, Peter Triantafillou, Isabelle Guyon
Are we making progress in unlearning? Findings from the first NeurIPS unlearning competition. CoRR (2024)
Devin Kwok, Nikhil Anand, Jonathan Frankle, Gintare Karolina Dziugaite, David Rolnick
Dataset Difficulty and the Role of Inductive Bias. CoRR (2024)
Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
Leveraging Function Space Aggregation for Federated Learning at Scale. Trans. Mach. Learn. Res. 2024 (2024)
Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias. AISTATS (2024)
Johan S. Obando-Ceron, Ghada Sokar, Timon Willi, Clare Lyle, Jesse Farebrother, Jakob N. Foerster, Gintare Karolina Dziugaite, Doina Precup, Pablo Samuel Castro
Mixtures of Experts Unlock Parameter Scaling for Deep RL. CoRR (2024)
Idan Attias, Gintare Karolina Dziugaite, Mahdi Haghifam, Roi Livni, Daniel M. Roy
Information Complexity of Stochastic Convex Optimization: Applications to Generalization and Memorization. CoRR (2024)
Tian Jin, Nolan Clement, Xin Dong, Vaishnavh Nagarajan, Michael Carbin, Jonathan Ragan-Kelley, Gintare Karolina Dziugaite
The Cost of Down-Scaling Language Models: Fact Recall Deteriorates before In-Context Learning. CoRR (2023)
Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. ALT (2023)
Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? ICLR (2023)
Nikita Dhawan, Nicole Mitchell, Zachary Charles, Zachary Garrett, Gintare Karolina Dziugaite
Leveraging Function Space Aggregation for Federated Learning at Scale. CoRR (2023)
Yu Yang, Eric Gan, Gintare Karolina Dziugaite, Baharan Mirzasoleiman
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias. CoRR (2023)
Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
Pruning's Effect on Generalization Through the Lens of Training and Regularization. NeurIPS (2022)
Zachary Ankner, Alex Renda, Gintare Karolina Dziugaite, Jonathan Frankle, Tian Jin
The Effect of Data Dimensionality on Neural Network Prunability. CoRR (2022)
Mahdi Haghifam, Borja Rodríguez Gálvez, Ragnar Thobaben, Mikael Skoglund, Daniel M. Roy, Gintare Karolina Dziugaite
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization. CoRR (2022)
Mansheej Paul, Feng Chen, Brett W. Larsen, Jonathan Frankle, Surya Ganguli, Gintare Karolina Dziugaite
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask? CoRR (2022)
Mahdi Haghifam, Shay Moran, Daniel M. Roy, Gintare Karolina Dziugaite
Understanding Generalization via Leave-One-Out Conditional Mutual Information. CoRR (2022)
Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. CoRR (2022)
Tian Jin, Michael Carbin, Daniel M. Roy, Jonathan Frankle, Gintare Karolina Dziugaite
Pruning's Effect on Generalization Through the Lens of Training and Regularization. CoRR (2022)
Mansheej Paul, Brett W. Larsen, Surya Ganguli, Jonathan Frankle, Gintare Karolina Dziugaite
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks. NeurIPS (2022)
Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy
Towards a Unified Information-Theoretic Framework for Generalization. CoRR (2021)
Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Dan Roy
Towards a Unified Information-Theoretic Framework for Generalization. NeurIPS (2021)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel Roy, Michael Carbin
Pruning Neural Networks at Initialization: Why Are We Missing the Mark? ICLR (2021)
Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite
Deep Learning on a Data Diet: Finding Important Examples Early in Training. NeurIPS (2021)
Soufiane Hayou, Bobby He, Gintare Karolina Dziugaite
Probabilistic fine-tuning of pruning masks and PAC-Bayes self-bounded learning. CoRR (2021)
Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Gabriel Arpino, Daniel Roy
On the role of data in PAC-Bayes. AISTATS (2021)
Mansheej Paul, Surya Ganguli, Gintare Karolina Dziugaite
Deep Learning on a Data Diet: Finding Important Examples Early in Training. CoRR (2021)
Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel Roy
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors. ICML (2020)
Yiding Jiang, Parth Natekar, Manik Sharma, Sumukh K. Aithal, Dhruva Kashyap, Natarajan Subramanyam, Carlos Lassance, Daniel M. Roy, Gintare Karolina Dziugaite, Suriya Gunasekar, Isabelle Guyon, Pierre Foret, Scott Yak, Hossein Mobahi, Behnam Neyshabur, Samy Bengio
Methods and Analysis of The First Competition in Predicting Generalization of Deep Learning. NeurIPS (Competition and Demos) (2020)
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
In search of robust measures of generalization. NeurIPS (2020)
Gintare Karolina Dziugaite, Kyle Hsu, Waseem Gharbieh, Daniel M. Roy
On the role of data in PAC-Bayes bounds. CoRR (2020)
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. NeurIPS (2020)
Gintare Karolina Dziugaite, Shai Ben-David, Daniel M. Roy
Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability. CoRR (2020)
Stanislav Fort, Gintare Karolina Dziugaite, Mansheej Paul, Sepideh Kharaghani, Daniel M. Roy, Surya Ganguli
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel. CoRR (2020)
Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. NeurIPS (2020)
Yiding Jiang, Pierre Foret, Scott Yak, Daniel M. Roy, Hossein Mobahi, Gintare Karolina Dziugaite, Samy Bengio, Suriya Gunasekar, Isabelle Guyon, Behnam Neyshabur
NeurIPS 2020 Competition: Predicting Generalization in Deep Learning. CoRR (2020)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
Linear Mode Connectivity and the Lottery Ticket Hypothesis. ICML (2020)
Gintare Karolina Dziugaite, Alexandre Drouin, Brady Neal, Nitarshan Rajkumar, Ethan Caballero, Linbo Wang, Ioannis Mitliagkas, Daniel M. Roy
In Search of Robust Measures of Generalization. CoRR (2020)
Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron C. Courville
Stochastic Neural Network with Kronecker Flow. AISTATS (2020)
Mahdi Haghifam, Jeffrey Negrea, Ashish Khisti, Daniel M. Roy, Gintare Karolina Dziugaite
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms. CoRR (2020)
Elnaz Barshan, Marc-Etienne Brunet, Gintare Karolina Dziugaite
RelatIF: Identifying Explanatory Training Samples via Relative Influence. AISTATS (2020)
Elnaz Barshan, Marc-Etienne Brunet, Gintare Karolina Dziugaite
RelatIF: Identifying Explanatory Training Examples via Relative Influence. CoRR (2020)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
Pruning Neural Networks at Initialization: Why are We Missing the Mark? CoRR (2020)
Mahdi Haghifam, Gintare Karolina Dziugaite, Shay Moran, Daniel M. Roy
On the Information Complexity of Proper Learners for VC Classes in the Realizable Case. CoRR (2020)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
Linear Mode Connectivity and the Lottery Ticket Hypothesis. CoRR (2019)
Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel M. Roy
In Defense of Uniform Convergence: Generalization via derandomization with an application to interpolating predictors. CoRR (2019)
Chin-Wei Huang, Ahmed Touati, Pascal Vincent, Gintare Karolina Dziugaite, Alexandre Lacoste, Aaron C. Courville
Stochastic Neural Network with Kronecker Flow. CoRR (2019)
Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. NeurIPS (2019)
Jeffrey Negrea, Mahdi Haghifam, Gintare Karolina Dziugaite, Ashish Khisti, Daniel M. Roy
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates. CoRR (2019)
Jonathan Frankle, Gintare Karolina Dziugaite, Daniel M. Roy, Michael Carbin
The Lottery Ticket Hypothesis at Scale. CoRR (2019)
Gintare Karolina Dziugaite, Daniel M. Roy
Data-dependent PAC-Bayes priors via differential privacy. NeurIPS (2018)
Gintare Karolina Dziugaite, Daniel M. Roy
Data-dependent PAC-Bayes priors via differential privacy. CoRR (2018)
Gintare Karolina Dziugaite, Daniel M. Roy
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors. ICML (2018)
Gintare Karolina Dziugaite, Daniel M. Roy
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data. CoRR (2017)
Gintare Karolina Dziugaite, Daniel M. Roy
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Data-dependent PAC-Bayes priors via differential privacy. CoRR (2017)
Gintare Karolina Dziugaite, Daniel M. Roy
Computing Nonvacuous Generalization Bounds for Deep (Stochastic) Neural Networks with Many More Parameters than Training Data. UAI (2017)
Gintare Karolina Dziugaite, Zoubin Ghahramani, Daniel M. Roy
A study of the effect of JPG compression on adversarial images. CoRR (2016)
Gintare Karolina Dziugaite, Daniel M. Roy
Neural Network Matrix Factorization. CoRR (2015)
Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani
Training generative neural networks via Maximum Mean Discrepancy optimization. UAI (2015)
Gintare Karolina Dziugaite, Daniel M. Roy, Zoubin Ghahramani
Training generative neural networks via Maximum Mean Discrepancy optimization. CoRR (2015)