63 citations · 97 across the 21 of their papers we have counts for
5 papers · 1 filter
Robustness and Personalization in Federated Learning: A Unified Approach via Regularization
Achintya Kundu, Pengqian Yu, Laura Wynter +1
We present a class of methods for robust, personalized federated learning, called Fed+, that unifies many federated learning algorithms. The principal advantage of this class of me…
Mutual Information Maximization in Graph Neural Networks
Xinhan Di, Pengqian Yu, Rui Bu +1
A variety of graph neural networks (GNNs) frameworks for representation learning on graphs have been recently developed. These frameworks rely on aggregation and iteration scheme t…
Model-based Deep Reinforcement Learning for Dynamic Portfolio Optimization
Pengqian Yu, Joon Sern Lee, Ilya Kulyatin +2
Dynamic portfolio optimization is the process of sequentially allocating wealth to a collection of assets in some consecutive trading periods, based on investors' return-risk profi…
Ambient Hidden Space of Generative Adversarial Networks
Xinhan Di, Pengqian Yu, Meng Tian
Generative adversarial models are powerful tools to model structure in complex distributions for a variety of tasks. Current techniques for learning generative models require an ac…
Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification
Xinhan Di, Pengqian Yu, Meng Tian
It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process. The gap between robust deep learning systems…