16 citations · 24 across the 4 of their papers we have counts for
6 papers
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 $…
Textual Analysis of Communications in COVID-19 Infected Community on Social Media
Yuhan Liu, Yuhan Gao, Zhifan Nan +1
During the COVID-19 pandemic, people started to discuss about pandemic-related topics on social media. On subreddit \textit{r/COVID19positive}, a number of topics are discussed or…
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…
Estimating Sparse Discrete Distributions Under Local Privacy and Communication Constraints
Jayadev Acharya, Peter Kairouz, Yuhan Liu +1
We consider the problem of estimating sparse discrete distributions under local differential privacy (LDP) and communication constraints. We characterize the sample complexity for…
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…