1 citations · 1 across the 6 of their papers we have counts for
6 papers
Bayesian Meta-Learning with Expert Feedback for Task-Shift Adaptation through Causal Embeddings
Lotta Mäkinen, Jorge Loría, 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…
Incorporating Expert Knowledge into Bayesian Causal Discovery of Mixtures of Directed Acyclic Graphs
Zachris Björkman, Jorge Loría, 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…
Deep Kernel Posterior Learning under Infinite Variance Prior Weights
Jorge Loría, 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…
Posterior Inference on Shallow Infinitely Wide Bayesian Neural Networks under Weights with Unbounded Variance
Jorge Loría, Anindya Bhadra
From the classical and influential works of Neal (1996), it is known that the infinite width scaling limit of a Bayesian neural network with one hidden layer is a Gaussian process,…
SURE-tuned Bridge Regression
Jorge Loría, Anindya Bhadra
Consider the {} regularized linear regression, also termed Bridge regression. For , Bridge regression enjoys several statistical properties of interest such as…
Causal Ordering Without Effect Estimation: A Framework for Using Proxies in Treatment Prioritization
Carlos Fernández-Loría, Jorge Loría
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…