Gintare Karolina Dziugaite
28 papers · 2018–2025 · 6 venues · across top CS/AI venues
Authors are matched by name, so researchers who share a name ("Yang Liu", "Wei Wang") appear as a single profile and their paper counts, venues and achievements are combined. How identity is handled.
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Research topics
Machine Learning
(26)
Optimization & Theory · Machine Learning
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Deep Learning
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Artificial Intelligence
(6)
Mathematics & Optimization
(6)
Application Areas
(6)
Core AI
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Optimization & Theory · Deep Learning
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Learning Types · Machine Learning
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Mathematics
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Learning Types · Deep Learning
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Optimization
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Probability
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Keywords
generalization bound
(9)
neural network pruning
(5)
pac-bayes bound
(5)
generalization error
(4)
model compression
(4)
lottery ticket hypothesis
(3)
iterative magnitude pruning
(3)
stochastic gradient langevin dynamics
(3)
conditional mutual information
(3)
scaling law
(2)
training dynamics
(2)
excess risk
(2)
differential privacy
(2)
posterior distribution
(2)
stochastic convex optimization
(2)
stochastic gradient descent
(2)
loss landscape
(2)
gradient descent
(2)
information theory
(2)
mutual information
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Papers
Leveraging Per-Instance Privacy for Machine Unlearning
ICML 2025
Mechanistic Unlearning: Robust Knowledge Unlearning and Editing via Mechanistic Localization
ICML 2025
Selective Unlearning via Representation Erasure Using Domain Adversarial Training
ICLR 2025
The Journey Matters: Average Parameter Count over Pre-training Unifies Sparse and Dense Scaling Laws
ICLR 2025
The Size of Teachers as a Measure of Data Complexity: PAC-Bayes Excess Risk Bounds and Scaling Laws
AISTATS 2025
Identifying Spurious Biases Early in Training through the Lens of Simplicity Bias
AISTATS 2024
Information Complexity of Stochastic Convex Optimization: Applications to Generalization, Memorization, and Tracing
ICML 2024
Mixtures of Experts Unlock Parameter Scaling for Deep RL
ICML 2024
The Cost of Scaling Down Large Language Models: Reducing Model Size Affects Memory before In-context Learning
ICLR 2024
Limitations of Information-Theoretic Generalization Bounds for Gradient Descent Methods in Stochastic Convex Optimization
ALT 2023
Unmasking the Lottery Ticket Hypothesis: What's Encoded in a Winning Ticket's Mask?
ICLR 2023
Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks
NeurIPS 2022
Pruning’s Effect on Generalization Through the Lens of Training and Regularization
NeurIPS 2022
Deep Learning on a Data Diet: Finding Important Examples Early in Training
NeurIPS 2021
Information-Theoretic Generalization Bounds for Stochastic Gradient Descent
COLT 2021
On the Role of Data in PAC-Bayes Bounds
AISTATS 2021
Pruning Neural Networks at Initialization: Why Are We Missing the Mark?
ICLR 2021
Towards a Unified Information-Theoretic Framework for Generalization
NeurIPS 2021
Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel
NeurIPS 2020
In Defense of Uniform Convergence: Generalization via Derandomization with an Application to Interpolating Predictors
ICML 2020
In search of robust measures of generalization
NeurIPS 2020
Linear Mode Connectivity and the Lottery Ticket Hypothesis
ICML 2020
RelatIF: Identifying Explanatory Training Samples via Relative Influence
AISTATS 2020
Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms
NeurIPS 2020
Stochastic Neural Network with Kronecker Flow
AISTATS 2020
Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates
NeurIPS 2019
Data-dependent PAC-Bayes priors via differential privacy
NeurIPS 2018
Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors
ICML 2018