Martin Gubri

Orcid: 0000-0001-6744-6662

According to our database1, Martin Gubri authored at least 13 papers between 2018 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

Online presence:

On csauthors.net:

Bibliography

2024
Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models.
CoRR, 2024

Calibrating Large Language Models Using Their Generations Only.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification.
Proceedings of the Findings of the Association for Computational Linguistics, 2024

2023
What Matters in Model Training to Transfer Adversarial Examples.
PhD thesis, 2023

Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability.
CoRR, 2023

ProPILE: Probing Privacy Leakage in Large Language Models.
Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022
Efficient and transferable adversarial examples from bayesian neural networks.
Proceedings of the Uncertainty in Artificial Intelligence, 2022

LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity.
Proceedings of the Computer Vision - ECCV 2022, 2022

Influence-driven data poisoning in graph-based semi-supervised classifiers.
Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI, 2022

2020
Effective and Efficient Data Poisoning in Semi-Supervised Learning.
CoRR, 2020

Efficient and Transferable Adversarial Examples from Bayesian Neural Networks.
CoRR, 2020

Search-based adversarial testing and improvement of constrained credit scoring systems.
Proceedings of the ESEC/FSE '20: 28th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 2020

2018
Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression.
CoRR, 2018


  Loading...