4 citations · 5 across the 13 of their papers we have counts for
4 papers · 1 filter
Relaxing Faithfulness with Intervention-Only Causal Discovery
Bijan Mazaheri, Jiaqi Zhang, Caroline Uhler
Causal discovery algorithms learn a network that describes the causal dependencies among random variables. A common workflow involves first utilizing conditional independence prope…
CountsDiff: A Diffusion Model on the Natural Numbers for Generation and Imputation of Count-Based Data
Renzo G. Soatto, Anders Hoel, Greycen Ren +5
Diffusion models have excelled at generative tasks for both continuous and token-based domains, but their application to discrete ordinal data remains underdeveloped. We present Co…
On the Number of Conditional Independence Tests in Constraint-based Causal Discovery
Marc Franquesa Monés, Jiaqi Zhang, Caroline Uhler
Learning causal relations from observational data is a fundamental problem with wide-ranging applications across many fields. Constraint-based methods infer the underlying causal s…
Latent Causal Diffusions for Single-Cell Perturbation Modeling
Lars Lorch, Jiaqi Zhang, Charlotte Bunne +3
Perturbation screens hold the potential to systematically map regulatory processes at single-cell resolution, yet modeling and predicting transcriptome-wide responses to perturbati…