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Found 8 papers, 0 papers with code
Scalable Structure Learning for Sparse Context-Specific Systems
Scalable learning is achieved through a combination of an order-based Markov chain Monte-Carlo search and a novel, context-specific sparsity assumption that is analogous to those typically invoked for directed acyclic graphical models.
Neuro-Causal Factor Analysis
Factor analysis (FA) is a statistical tool for studying how observed variables with some mutual dependences can be expressed as functions of mutually independent unobserved factors, and it is widely applied throughout the psychological, biological, and physical sciences.
Queer In AI: A Case Study in Community-Led Participatory AI
We present Queer in AI as a case study for community-led participatory design in AI.
Combinatorial and algebraic perspectives on the marginal independence structure of Bayesian networks
We consider the problem of estimating the marginal independence structure of a Bayesian network from observational data, learning an undirected graph we call the unconditional dependence graph.
A Transformational Characterization of Unconditionally Equivalent Bayesian Networks
We consider the problem of characterizing Bayesian networks up to unconditional equivalence, i. e., when directed acyclic graphs (DAGs) have the same set of unconditional $d$-separation statements.
A Distance Covariance-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
Even stronger, we prove that the kernel space is isometric to the space of causal ancestral graphs, so that distance between samples in the kernel space is guaranteed to correspond to distance between their generating causal structures.
A Distance Correlation-based Kernel for Nonlinear Causal Clustering in Heterogeneous Populations
We consider the problem of causal structure learning in the setting of heterogeneous populations, i. e., populations in which a single causal structure does not adequately represent all population members, as is common in biological and social sciences.
Measurement Dependence Inducing Latent Causal Models
We consider the task of causal structure learning over measurement dependence inducing latent (MeDIL) causal models.
