Lauren Watson

According to our database, Lauren Watson authored at least 10 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of three.

2024

Towards Understanding the Interplay of Generative Artificial Intelligence and the Internet.

Gonzalo Martínez, Lauren Watson, Pedro Reviriego, José Alberto Hernández, Marc Juarez, Rik Sarkar

Proceedings of the Epistemic Uncertainty in Artificial Intelligence, 2024

2023

Inference and Interference: The Role of Clipping, Pruning and Loss Landscapes in Differentially Private Stochastic Gradient Descent.

Lauren Watson, Eric Gan, Mohan Dantam, Baharan Mirzasoleiman, Rik Sarkar

CoRR, 2023

Accelerated Shapley Value Approximation for Data Evaluation.

Lauren Watson, Zeno Kujawa, Rayna Andreeva, Hao-Tsung Yang, Tariq Elahi, Rik Sarkar

CoRR, 2023

Combining Generative Artificial Intelligence (AI) and the Internet: Heading towards Evolution or Degradation?

Gonzalo Martínez, Lauren Watson, Pedro Reviriego, José Alberto Hernández, Marc Juarez, Rik Sarkar

CoRR, 2023

2022

Differentially Private Shapley Values for Data Evaluation.

Lauren Watson, Rayna Andreeva, Hao-Tsung Yang, Rik Sarkar

CoRR, 2022

Continual and Sliding Window Release for Private Empirical Risk Minimization.

Lauren Watson, Abhirup Ghosh, Benedek Rozemberczki, Rik Sarkar

CoRR, 2022

The Shapley Value in Machine Learning.

Benedek Rozemberczki, Lauren Watson, Péter Bayer, Hao-Tsung Yang, Oliver Kiss, Sebastian Nilsson, Rik Sarkar

Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, 2022

On the Importance of Difficulty Calibration in Membership Inference Attacks.

Lauren Watson, Chuan Guo, Graham Cormode, Alexandre Sablayrolles

Proceedings of the Tenth International Conference on Learning Representations, 2022

2020

Privacy Preserving Detection of Path Bias Attacks in Tor.

Lauren Watson, Anupam Mediratta, Tariq Elahi, Rik Sarkar

Proc. Priv. Enhancing Technol., 2020

Stability Enhanced Privacy and Applications in Private Stochastic Gradient Descent.

Lauren Watson, Benedek Rozemberczki, Rik Sarkar

CoRR, 2020