Alexander Marx


Postdoc Fellow


alexander.marx(at)inf.ethz.ch


ETH AI Center


ETH Zürich



Approximating Algorithmic Conditional Independence for Discrete Data


Conference paper


Alexander Marx, Jilles Vreeken
First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI, Stanford, 2019

R-Project
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APA
Marx, A., & Vreeken, J. (2019). Approximating Algorithmic Conditional Independence for Discrete Data. In First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-based AI, Stanford.

Chicago/Turabian
Marx, Alexander, and Jilles Vreeken. “Approximating Algorithmic Conditional Independence for Discrete Data.” In First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-Based AI, Stanford, 2019.

MLA
Marx, Alexander, and Jilles Vreeken. “Approximating Algorithmic Conditional Independence for Discrete Data.” First AAAI Spring Symposium Beyond Curve Fitting: Causation, Counterfactuals, and Imagination-Based AI, Stanford, 2019.


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