48 citations · 119 across the 12 of their papers we have counts for
7 papers · 1 filter
Hierarchical Variational Auto-Encoding for Unsupervised Domain Generalization
Xudong Sun, Florian Buettner
We address the task of domain generalization, where the goal is to train a predictive model such that it is able to generalize to a new, previously unseen domain. We choose a hiera…
More Industry-friendly: Federated Learning with High Efficient Design
Dingwei Li, Qinglong Chang, Lixue Pang +4
Although many achievements have been made since Google threw out the paradigm of federated learning (FL), there still exists much room for researchers to optimize its efficiency. I…
Tutorial and Survey on Probabilistic Graphical Model and Variational Inference in Deep Reinforcement Learning
Xudong Sun, Bernd Bischl
Aiming at a comprehensive and concise tutorial survey, recap of variational inference and reinforcement learning with Probabilistic Graphical Models are given with detailed derivat…
Variational Resampling Based Assessment of Deep Neural Networks under Distribution Shift
Xudong Sun, Alexej Gossmann, Yu Wang +1
A novel variational inference based resampling framework is proposed to evaluate the robustness and generalization capability of deep learning models with respect to distribution s…
Maximum Entropy-Regularized Multi-Goal Reinforcement Learning
Rui Zhao, Xudong Sun, Volker Tresp
In Multi-Goal Reinforcement Learning, an agent learns to achieve multiple goals with a goal-conditioned policy. During learning, the agent first collects the trajectories into a re…
ReinBo: Machine Learning pipeline search and configuration with Bayesian Optimization embedded Reinforcement Learning
Xudong Sun, Jiali Lin, Bernd Bischl
Machine learning pipeline potentially consists of several stages of operations like data preprocessing, feature engineering and machine learning model training. Each operation has…
High Dimensional Restrictive Federated Model Selection with multi-objective Bayesian Optimization over shifted distributions
Xudong Sun, Andrea Bommert, Florian Pfisterer +3
A novel machine learning optimization process coined Restrictive Federated Model Selection (RFMS) is proposed under the scenario, for example, when data from healthcare units can n…