Caroline Uhler

Ordering-Based Causal Structure Learning in the Presence of Latent Variables

2019 · arXiv (Cornell University)

We consider the task of learning a causal graph in the presence of latent confounders given i.i.d.~samples from the model. While current algorithms for causal structure discovery in the presence of latent confounders are constraint-based, …

Transfer Learning with Kernel Methods

2022 · arXiv (Cornell University)

Transfer learning refers to the process of adapting a model trained on a source task to a target task. While kernel methods are conceptually and computationally simple machine learning models that are competitive on a …

Identifiability Guarantees for Causal Disentanglement from Purely Observational Data

2024 · arXiv (Cornell University)

Causal disentanglement aims to learn about latent causal factors behind data, holding the promise to augment existing representation learning methods in terms of interpretability and extrapolation. Recent advances establish identifiability results assuming that interventions on …