395 citations · 969 across the 46 of their papers we have counts for
12 papers · 1 filter
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…
HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients
Enmao Diao, Jie Ding, Vahid Tarokh
Federated Learning (FL) is a method of training machine learning models on private data distributed over a large number of possibly heterogeneous clients such as mobile phones and…
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…
Forecasting with Multiple Seasonality
Tianyang Xie, Jie Ding
An emerging number of modern applications involve forecasting time series data that exhibit both short-time dynamics and long-time seasonality. Specifically, time series with multi…
Fisher Auto-Encoders
Khalil Elkhalil, Ali Hasan, Jie Ding +2
It has been conjectured that the Fisher divergence is more robust to model uncertainty than the conventional Kullback-Leibler (KL) divergence. This motivates the design of a new cl…
Meta Clustering for Collaborative Learning
Chenglong Ye, Reza Ghanadan, Jie Ding
In collaborative learning, learners coordinate to enhance each of their learning performances. From the perspective of any learner, a critical challenge is to filter out unqualifie…
Model Linkage Selection for Cooperative Learning
Jiaying Zhou, Jie Ding, Kean Ming Tan +1
We consider a distributed learning setting where each agent/learner holds a specific parametric model and data source. The goal is to integrate information across a set of learners…
Assisted Learning: A Framework for Multi-Organization Learning
Xun Xian, Xinran Wang, Jie Ding +1
In an increasing number of AI scenarios, collaborations among different organizations or agents (e.g., human and robots, mobile units) are often essential to accomplish an organiza…
Multimodal Controller for Generative Models
Enmao Diao, Jie Ding, Vahid Tarokh
Class-conditional generative models are crucial tools for data generation from user-specified class labels. Existing approaches for class-conditional generative models require nont…