Multiple Distribution

Multiple distribution learning focuses on developing methods to effectively learn from and make predictions across datasets originating from multiple, potentially different, probability distributions. Current research emphasizes efficient algorithms for handling these diverse data sources, including the development of novel divergence measures and the adaptation of existing models like variational Bayes and graph neural networks to this multi-distribution setting. This field is crucial for improving the robustness and generalizability of machine learning models in real-world applications where data heterogeneity is common, such as in federated learning, anomaly detection, and causal inference. The ultimate goal is to create models that perform well across a range of distributions without requiring extensive retraining or modification for each new distribution encountered.