Lauren Watson
Cited by
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The shapley value in machine learning
B Rozemberczki, L Watson, P Bayer, HT Yang, O Kiss, S Nilsson, ...
International Joint Conference on Artificial Intelligence, 2022
On the importance of difficulty calibration in membership inference attacks
L Watson, C Guo, G Cormode, A Sablayrolles
International Conference on Learning Representations, 2021
Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet
G Martínez, L Watson, P Reviriego, JA Hernández, M Juarez, R Sarkar
E-pi UAI, 2023
Differentially private shapley values for data evaluation
L Watson, R Andreeva, HT Yang, R Sarkar
arXiv preprint arXiv:2206.00511, 2022
Privacy Preserving Detection of Path Bias Attacks in Tor
L Watson, A Mediratta, T Elahi, R Sarkar
Proceedings on Privacy Enhancing Technologies 2020 (4), 111-130, 2020
Stability enhanced privacy and applications in private stochastic gradient descent
L Watson, B Rozemberczki, R Sarkar
arXiv preprint arXiv:2006.14360, 2020
Multi-task learning for sequence-to-sequence neural models of lemmatization
L Watson
Master’s thesis, University of Edinburgh, 2018
Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent
L Watson, E Gan, M Dantam, B Mirzasoleiman, R Sarkar
arXiv preprint arXiv:2311.06839, 2023
Accelerated Shapley Value Approximation for Data Evaluation
L Watson, Z Kujawa, R Andreeva, HT Yang, T Elahi, R Sarkar
arXiv preprint arXiv:2311.05346, 2023
Continual and Sliding Window Release for Private Empirical Risk Minimization
L Watson, A Ghosh, B Rozemberczki, R Sarkar
arXiv preprint arXiv:2203.03594, 2022
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