18 citations · 55 across the 14 of their papers we have counts for
34 papers
Towards Differential Relational Privacy and its use in Question Answering
Simone Bombari, Alessandro Achille, Zijian Wang +6
Memorization of the relation between entities in a dataset can lead to privacy issues when using a trained model for question answering. We introduce Relational Memorization (RM) t…
Adaptive Private-K-Selection with Adaptive K and Application to Multi-label PATE
Yuqing Zhu, Yu-Xiang Wang
We provide an end-to-end Renyi DP based-framework for differentially private top- selection. Unlike previous approaches, which require a data-independent choice on , we propo…
Mixed Differential Privacy in Computer Vision
Aditya Golatkar, Alessandro Achille, Yu-Xiang Wang +3
We introduce AdaMix, an adaptive differentially private algorithm for training deep neural network classifiers using both private and public image data. While pre-training language…
Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism
Ming Yin, Yaqi Duan, Mengdi Wang +1
Offline reinforcement learning, which seeks to utilize offline/historical data to optimize sequential decision-making strategies, has gained surging prominence in recent studies. D…
Optimal Dynamic Regret in Proper Online Learning with Strongly Convex Losses and Beyond
Dheeraj Baby, Yu-Xiang Wang
We study the framework of universal dynamic regret minimization with strongly convex losses. We answer an open problem in Baby and Wang 2021 by showing that in a proper learning se…
Privately Publishable Per-instance Privacy
Rachel Redberg, Yu-Xiang Wang
We consider how to privately share the personalized privacy losses incurred by objective perturbation, using per-instance differential privacy (pDP). Standard differential privacy…