175 citations · 531 across the 38 of their papers we have counts for
10 papers · 1 filter
Towards More Robust Natural Language Understanding
Xinliang Frederick Zhang
Natural Language Understanding (NLU) is a branch of Natural Language Processing (NLP) that uses intelligent computer software to understand texts that encode human knowledge. Recen…
Zero-Round Active Learning
Si Chen, Tianhao Wang, Ruoxi Jia
Active learning (AL) aims at reducing labeling effort by identifying the most valuable unlabeled data points from a large pool. Traditional AL frameworks have two limitations: Firs…
Improving Cooperative Game Theory-based Data Valuation via Data Utility Learning
Tianhao Wang, Yu Yang, Ruoxi Jia
The Shapley value (SV) and Least core (LC) are classic methods in cooperative game theory for cost/profit sharing problems. Both methods have recently been proposed as a principled…
DPSyn: Experiences in the NIST Differential Privacy Data Synthesis Challenges
Ninghui Li, Zhikun Zhang, Tianhao Wang
We summarize the experience of participating in two differential privacy competitions organized by the National Institute of Standards and Technology (NIST). In this paper, we docu…
A Unified Framework for Task-Driven Data Quality Management
Tianhao Wang, Yi Zeng, Ming Jin +1
High-quality data is critical to train performant Machine Learning (ML) models, highlighting the importance of Data Quality Management (DQM). Existing DQM schemes often cannot sati…
Differential Privacy for Text Analytics via Natural Text Sanitization
Xiang Yue, Minxin Du, Tianhao Wang +3
Texts convey sophisticated knowledge. However, texts also convey sensitive information. Despite the success of general-purpose language models and domain-specific mechanisms with d…
Concurrent Composition of Differential Privacy
Salil Vadhan, Tianhao Wang
We initiate a study of the composition properties of interactive differentially private mechanisms. An interactive differentially private mechanism is an algorithm that allows an a…
One-Round Active Learning
Tianhao Wang, Si Chen, Ruoxi Jia
In this work, we initiate the study of one-round active learning, which aims to select a subset of unlabeled data points that achieve the highest model performance after being labe…
Graph Unlearning
Min Chen, Zhikun Zhang, Tianhao Wang +3
Machine unlearning is a process of removing the impact of some training data from the machine learning (ML) models upon receiving removal requests. While straightforward and legiti…
DPlis: Boosting Utility of Differentially Private Deep Learning via Randomized Smoothing
Wenxiao Wang, Tianhao Wang, Lun Wang +4
Deep learning techniques have achieved remarkable performance in wide-ranging tasks. However, when trained on privacy-sensitive datasets, the model parameters may expose private in…