4 papers
Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
Zachris Björkman, Jorge LorÃa, Sophie Wharrie +1
Bayesian causal discovery benefits from prior information elicited from domain experts, and in heterogeneous domains any prior knowledge would be badly needed. However, so far prio…
Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
Lotta Mäkinen, Jorge LorÃa, Samuel Kaski
Meta-learning methods perform well on new within-distribution tasks but often fail when adapting to out-of-distribution target tasks, where transfer from source tasks can induce ne…
Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
Carlos Fernández-LorÃa, Jorge LorÃa
Who should we prioritize for treatment when causal effects cannot be estimated? In practice, organizations often rely on predictive proxies: ads are targeted using purchase probabi…
Deep Kernel Posterior Learning under Infinite Variance Prior Weights
Jorge LorÃa, Anindya Bhadra
Neal (1996) proved that infinitely wide shallow Bayesian neural networks (BNN) converge to Gaussian processes (GP), when the network weights have bounded prior variance. Cho & Saul…