conditional independence
In probabilistic graphical models and machine learning, conditional independence indicates that two random variables are independent given the value of a third variable, which aids in simplifying models and computations by reducing the complexity of joint probability distributions.
- Can Dependencies Induced by LLM-Agent Workflows Be Trusted?
- Characterization and Learning of Causal Graphs from Hard Interventions
- Distributional Autoencoders Know the Score
- Joint Relational Database Generation via Graph-Conditional Diffusion Models
- Less Greedy Equivalence Search
- Neurosymbolic Diffusion Models
- On the Hardness of Conditional Independence Testing In Practice
- Practical Kernel Selection for Kernel-based Conditional Independence Test