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09/20/2023
Federated Learning with Neural Graphical Models
Federated Learning (FL) addresses the need to create models based on pro...
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08/10/2023
Knowledge Propagation over Conditional Independence Graphs
Conditional Independence (CI) graph is a special type of a Probabilistic...
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04/23/2023
Missing Values and Imputation in Healthcare Data: Can Interpretable Machine Learning Help?
Missing values are a fundamental problem in data science. Many datasets ...
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02/27/2023
Neural Graph Revealers
Sparse graph recovery methods work well where the data follows their ass...
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11/13/2022
Methods for Recovering Conditional Independence Graphs: A Survey
Conditional Independence (CI) graphs are a type of probabilistic graphic...
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10/02/2022
Neural Graphical Models
Graphs are ubiquitous and are often used to understand the dynamics of a...
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05/23/2022
uGLAD: Sparse graph recovery by optimizing deep unrolled networks
Probabilistic Graphical Models (PGMs) are generative models of complex s...
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02/04/2022
Discovering Distribution Shifts using Latent Space Representations
Rapid progress in representation learning has led to a proliferation of ...
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10/22/2018
Interpretability is Harder in the Multiclass Setting: Axiomatic Interpretability for Multiclass Additive Models
Generalized additive models (GAMs) are favored in many regression and bi...
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02/06/2013
Defining Explanation in Probabilistic Systems
As probabilistic systems gain popularity and are coming into wider use, ...
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01/30/2013
Utility Elicitation as a Classification Problem
We investigate the application of classification techniques to utility e...
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01/16/2013
Utilities as Random Variables: Density Estimation and Structure Discovery
Decision theory does not traditionally include uncertainty over utility ...
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01/10/2013
