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Found 9 papers, 6 papers with code
Federated Learning with Neural Graphical Models
We develop a FL framework which maintains a global NGM model that learns the averaged information from the local NGM models while keeping the training data within the client's environment.
Knowledge Propagation over Conditional Independence Graphs
Conditional Independence (CI) graph is a special type of a Probabilistic Graphical Model (PGM) where the feature connections are modeled using an undirected graph and the edge weights show the partial correlation strength between the features.
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Missing values are a fundamental problem in data science.
Neural Graph Revealers
Sparse graph recovery methods work well where the data follows their assumptions but often they are not designed for doing downstream probabilistic queries.
Methods for Recovering Conditional Independence Graphs: A Survey
Conditional Independence (CI) graphs are a type of probabilistic graphical models that are primarily used to gain insights about feature relationships.
Neural Graphical Models
Theoretically these models can represent very complex dependency functions, but in practice often simplifying assumptions are made due to computational limitations associated with graph operations.
uGLAD: Sparse graph recovery by optimizing deep unrolled networks
Our model, uGLAD, builds upon and extends the state-of-the-art model GLAD to the unsupervised setting.
Discovering Distribution Shifts using Latent Space Representations
Rapid progress in representation learning has led to a proliferation of embedding models, and to associated challenges of model selection and practical application.
Axiomatic Interpretability for Multiclass Additive Models
In the first part of this paper, we generalize a state-of-the-art GAM learning algorithm based on boosted trees to the multiclass setting, and show that this multiclass algorithm outperforms existing GAM learning algorithms and sometimes matches the performance of full complexity models such as gradient boosted trees.
