12 citations · 64 across the 17 of their papers we have counts for
17 papers
Publicly-Detectable Watermarking for Language Models
Jaiden Fairoze, Sanjam Garg, Somesh Jha +3
We present a publicly-detectable watermarking scheme for LMs: the detection algorithm contains no secret information, and it is executable by anyone. We embed a publicly-verifiable…
On Optimal Learning Under Targeted Data Poisoning
Steve Hanneke, Amin Karbasi, Mohammad Mahmoody +2
Consider the task of learning a hypothesis class in the presence of an adversary that can replace up to an fraction of the examples in the training set with arbit…
Overparameterization from Computational Constraints
Sanjam Garg, Somesh Jha, Saeed Mahloujifar +2
Overparameterized models with millions of parameters have been hugely successful. In this work, we ask: can the need for large models be, at least in part, due to the \emph{computa…
Deletion Inference, Reconstruction, and Compliance in Machine (Un)Learning
Ji Gao, Sanjam Garg, Mohammad Mahmoody +1
Privacy attacks on machine learning models aim to identify the data that is used to train such models. Such attacks, traditionally, are studied on static models that are trained on…
NeuraCrypt is not private
Nicholas Carlini, Sanjam Garg, Somesh Jha +3
NeuraCrypt (Yara et al. arXiv 2021) is an algorithm that converts a sensitive dataset to an encoded dataset so that (1) it is still possible to train machine learning models on the…
Learning and Certification under Instance-targeted Poisoning
Ji Gao, Amin Karbasi, Mohammad Mahmoody
In this paper, we study PAC learnability and certification of predictions under instance-targeted poisoning attacks, where the adversary who knows the test instance may change a fr…