Matthew Jagielski
19 papers · 2019–2025 · 6 conferences · across top CS/AI conferences
Achievements
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π Interdisciplinary Bridge π Academic Marathon (6) π Conference Polyglot (6) π Renaissance Researcher (5) πΊοΈ Taxonomy Completionist (15)
π§
Keyword Pioneer
π£
Hot Topic Early Bird
π
Conference Polyglot
(6)
π
Grand Slam
π€
Dynamic Duo
(10)
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Keyword Champion
(2)
β
The Questioner
(2)
β‘
Prolific Year
(6)
π
Century Club
(19)
Conferences
NIPS (6)
ICLR (5)
ICML (5)
AAAI (1)
ACL (1)
NAACL (1)
Top co-authors
Research topics
Keywords
differential privacy
(6)
training datum
(2)
large language model
(2)
privacy auditing
(2)
membership inference
(2)
machine learning
(2)
transfer learning
(1)
prototype learning
(1)
knowledge distillation
(1)
model distillation
(1)
machine unlearning
(1)
privacy-preserving learning
(1)
adversarial perturbation
(1)
model training
(1)
language model
(1)
privacy-utility trade-off
(1)
imbalanced learning
(1)
adversarial example
(1)
imbalanced datum
(1)
multimodal model
(1)
Papers
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
ICLR 2025
Scalable Extraction of Training Data from Aligned, Production Language Models
ICLR 2025
On Evaluating the Durability of Safeguards for Open-Weight LLMs
ICLR 2025
Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
ICML 2025
Differentially Private Prototypes for Imbalanced Transfer Learning
AAAI 2025
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training
ACL 2025
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
ICML 2025
Stealing part of a production language model
ICML 2024
Auditing Private Prediction
ICML 2024
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models
NAACL 2024
Quantifying Memorization Across Neural Language Models
ICLR 2023
Counterfactual Memorization in Neural Language Models
NIPS 2023
Students Parrot Their Teachers: Membership Inference on Model Distillation
NIPS 2023
Privacy Auditing with One (1) Training Run
NIPS 2023
Are aligned neural networks adversarially aligned?
NIPS 2023
Measuring Forgetting of Memorized Training Examples
ICLR 2023
The Privacy Onion Effect: Memorization is Relative
NIPS 2022
Auditing Differentially Private Machine Learning: How Private is Private SGD?
NIPS 2020
Differentially Private Fair Learning
ICML 2019