10 citations · 16 across the 7 of their papers we have counts for
18 papers
Learning Time Series from Scale Information
Yuan Yang, Jie Ding
Sequentially obtained dataset usually exhibits different behavior at different data resolutions/scales. Instead of inferring from data at each scale individually, it is often more…
On Statistical Efficiency in Learning
Jie Ding, Enmao Diao, Jiawei Zhou +1
A central issue of many statistical learning problems is to select an appropriate model from a set of candidate models. Large models tend to inflate the variance (or overfitting),…
ASCII: ASsisted Classification with Ignorance Interchange
Jiaying Zhou, Xun Xian, Na Li +1
The rapid development in data collecting devices and computation platforms produces an emerging number of agents, each equipped with a unique data modality over a particular popula…
Large Deviation Principle for the Whittaker 2d Growth Model
Jun Gao, Jie Ding
The Whittaker 2d growth model is a triangular continuous Markov diffusion process that appears in many scientific contexts. It has been theoretically intriguing to establish a larg…
Information Laundering for Model Privacy
Xinran Wang, Yu Xiang, Jun Gao +1
In this work, we propose information laundering, a novel framework for enhancing model privacy. Unlike data privacy that concerns the protection of raw data information, model priv…
Imitation Privacy
Xun Xian, Xinran Wang, Mingyi Hong +2
In recent years, there have been many cloud-based machine learning services, where well-trained models are provided to users on a pay-per-query scheme through a prediction API. The…