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 | 224* | 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 | 109 | 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 | 61* | 2023 |
Differentially private Shapley values for data evaluation L Watson, R Andreeva, HT Yang, R Sarkar arXiv preprint arXiv:2206.00511, 2022 | 7 | 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 | 2 | 2020 |
Stability enhanced privacy and applications in private stochastic gradient descent L Watson, B Rozemberczki, R Sarkar arXiv preprint arXiv:2006.14360, 2020 | 2 | 2020 |
Multi-task learning for sequence-to-sequence neural models of lemmatization L Watson Master’s thesis, University of Edinburgh, 2018 | 1 | 2018 |
Data privacy and valuation for trustworthy machine learning L Watson The University of Edinburgh, 2024 | | 2024 |
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 | | 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 | | 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 | | 2022 |