65 citations · 245 across the 28 of their papers we have counts for
15 papers · 1 filter
Stability Analysis Framework for Particle-based Distance GANs with Wasserstein Gradient Flow
Chuqi Chen, Yue Wu, Yang Xiang
In this paper, we investigate the training process of generative networks that use a type of probability density distance named particle-based distance as the objective function, e…
Personalized Federated Learning under Mixture of Distributions
Yue Wu, Shuaicheng Zhang, Wenchao Yu +5
The recent trend towards Personalized Federated Learning (PFL) has garnered significant attention as it allows for the training of models that are tailored to each client while mai…
Elastic Interaction Energy-Based Generative Model: Approximation in Feature Space
Chuqi Chen, Yue Wu, Yang Xiang
In this paper, we propose a novel approach to generative modeling using a loss function based on elastic interaction energy (EIE), which is inspired by the elastic interaction betw…
ADDS: Adaptive Differentiable Sampling for Robust Multi-Party Learning
Maoguo Gong, Yuan Gao, Yue Wu +1
Distributed multi-party learning provides an effective approach for training a joint model with scattered data under legal and practical constraints. However, due to the quagmire o…
Adaptive Sampling for Heterogeneous Rank Aggregation from Noisy Pairwise Comparisons
Yue Wu, Tao Jin, Hao Lou +3
In heterogeneous rank aggregation problems, users often exhibit various accuracy levels when comparing pairs of items. Thus a uniform querying strategy over users may not be optima…
Demonstration-Guided Reinforcement Learning with Learned Skills
Karl Pertsch, Youngwoon Lee, Yue Wu +1
Demonstration-guided reinforcement learning (RL) is a promising approach for learning complex behaviors by leveraging both reward feedback and a set of target task demonstrations.…