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Florian Tramèr
Florian Tramèr
Assistant Professor of Computer Science, ETH Zurich
Verifierad e-postadress på inf.ethz.ch - Startsida
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Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
Foundations and Trends® in Machine Learning 14 (1), 2019
66292019
On the opportunities and risks of foundation models
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
arXiv preprint arXiv:2108.07258, 2021
45942021
Ensemble Adversarial Training: Attacks and Defenses
F Tramèr, A Kurakin, N Papernot, I Goodfellow, D Boneh, P McDaniel
International Conference on Learning Representations (ICLR), 2018
34142018
Stealing Machine Learning Models via Prediction APIs
F Tramèr, F Zhang, A Juels, MK Reiter, T Ristenpart
25th USENIX security symposium (USENIX Security 16), 601-618, 2016
23382016
Extracting Training Data from Large Language Models
N Carlini, F Tramèr, E Wallace, M Jagielski, A Herbert-Voss, K Lee, ...
30th USENIX Security Symposium (USENIX Security 21), 2633--2650, 2021
18582021
On evaluating adversarial robustness
N Carlini, A Athalye, N Papernot, W Brendel, J Rauber, D Tsipras, ...
arXiv preprint arXiv:1902.06705, 2019
10342019
On adaptive attacks to adversarial example defenses
F Tramèr, N Carlini, W Brendel, A Madry
Conference on Neural Information Processing Systems (NeurIPS) 33, 2020
9392020
The space of transferable adversarial examples
F Tramèr, N Papernot, I Goodfellow, D Boneh, P McDaniel
arXiv preprint arXiv:1704.03453, 2017
6892017
Quantifying memorization across neural language models
N Carlini, D Ippolito, M Jagielski, K Lee, F Tramèr, C Zhang
International Conference on Learning Representations (ICLR), 2023
6462023
Membership Inference Attacks From First Principles
N Carlini, S Chien, M Nasr, S Song, A Terzis, F Tramèr
43rd IEEE Symposium on Security and Privacy (S&P 2022), 2022
6412022
Physical adversarial examples for object detectors
K Eykholt, I Evtimov, E Fernandes, B Li, A Rahmati, F Tramèr, A Prakash, ...
12th USENIX Workshop on Offensive Technologies (WOOT 18), 2018
5882018
Extracting training data from diffusion models
N Carlini, J Hayes, M Nasr, M Jagielski, V Sehwag, F Tramer, B Balle, ...
32nd USENIX Security Symposium (USENIX Security 23), 5253-5270, 2023
5702023
Label-Only Membership Inference Attacks
CAC Choo, F Tramèr, N Carlini, N Papernot
International Conference on Machine Learning (ICML), 1964--1974, 2021
525*2021
Slalom: Fast, verifiable and private execution of neural networks in trusted hardware
F Tramèr, D Boneh
International Conference on Learning Representations (ICLR), 2019
4742019
Adversarial training and robustness for multiple perturbations
F Tramèr, D Boneh
Conference on Neural Information Processing Systems (NeurIPS) 32, 2019
4372019
Advances and open problems in federated learning
P Kairouz, HB McMahan, B Avent, A Bellet, M Bennis, AN Bhagoji, ...
arXiv preprint arXiv:1912.04977, 0
396*
Sentinet: Detecting localized universal attacks against deep learning systems
E Chou, F Tramer, G Pellegrino
2020 IEEE Security and Privacy Workshops (SPW), 48-54, 2020
3872020
Large language models can be strong differentially private learners
X Li, F Tramèr, P Liang, T Hashimoto
International Conference on Learning Representations (ICLR), 2022
3422022
On the opportunities and risks of foundation models. CoRR abs/2108.07258 (2021)
R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ...
Preprint at https://arxiv. org/abs/2108.07258 2108, 2021
315*2021
Differentially Private Learning Needs Better Features (or Much More Data)
F Tramèr, D Boneh
International Conference on Learning Representations (ICLR), 2021
2722021
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Artiklar 1–20