17 citations · 43 across the 10 of their papers we have counts for
5 papers · 1 filter
Discrete Distribution Estimation under User-level Local Differential Privacy
Jayadev Acharya, Yuhan Liu, Ziteng Sun
We study discrete distribution estimation under user-level local differential privacy (LDP). In user-level -LDP, each user has samples and the privacy of all $…
Algorithms for bounding contribution for histogram estimation under user-level privacy
Yuhan Liu, Ananda Theertha Suresh, Wennan Zhu +2
We study the problem of histogram estimation under user-level differential privacy, where the goal is to preserve the privacy of all entries of any single user. We consider the het…
AutoFreeze: Automatically Freezing Model Blocks to Accelerate Fine-tuning
Yuhan Liu, Saurabh Agarwal, Shivaram Venkataraman
With the rapid adoption of machine learning (ML), a number of domains now use the approach of fine tuning models which were pre-trained on a large corpus of data. However, our expe…
Accelerating Deep Learning Inference via Learned Caches
Arjun Balasubramanian, Adarsh Kumar, Yuhan Liu +3
Deep Neural Networks (DNNs) are witnessing increased adoption in multiple domains owing to their high accuracy in solving real-world problems. However, this high accuracy has been…
Learning discrete distributions: user vs item-level privacy
Yuhan Liu, Ananda Theertha Suresh, Felix Yu +2
Much of the literature on differential privacy focuses on item-level privacy, where loosely speaking, the goal is to provide privacy per item or training example. However, recently…