Elina Robeva — accepted AI-conference papers

2025

Causal Discovery for Linear Non-Gaussian Models with Disjoint Cycles

Mathias Drton, Marina Garrote-López, Niko Nikov, Elina Robeva, Y. Samuel Wang

UAI 2025

The paradigm of linear structural equation modeling readily allows one to incorporate causal feedback loops in the model specification. These appear as directed cycles in the common graphical representation of the models. However, the presence of cycles entails difficulties such as the fact that mod

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2023

Ultra-marginal Feature Importance: Learning from Data with Causal Guarantees

Joseph Janssen, Vincent Guan, Elina Robeva

AISTATS 2023poster

Scientists frequently prioritize learning from data rather than training the best possible model; however, research in machine learning often prioritizes the latter. Marginal contribution feature importance (MCI) was developed to break this trend by providing a useful framework for quantifying the r…

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