4 papers
Anomaly detection in time-series via inductive biases in the latent space of conditional normalizing flows
David Baumgartner, Eliezer de Souza da Silva, Iñigo Urteaga
Deep generative models for anomaly detection in multivariate time-series are typically trained by maximizing observed data likelihood. However, likelihood in observation space meas…
Bayesian Variable Selection in Generalized Linear Models
Lucia Filippozzi, Iñigo Urteaga, Claudio Agostinelli
Covariate selection in Generalized Linear Models (GLMs) is a fundamental problem in statistics, as including irrelevant predictors might lead to overfitting and poor interpretabili…
Probabilistic Shapley Value Modeling and Inference
Mert Ketenci, Iñigo Urteaga, Victor Alfonso Rodriguez +2
We propose probabilistic Shapley inference (PSI), a novel probabilistic framework to model and infer sufficient statistics of feature attributions in flexible predictive models, vi…
Accurate and Scalable Stochastic Gaussian Process Regression via Learnable Coreset-based Variational Inference
Mert Ketenci, Adler Perotte, Noémie Elhadad +1
We introduce a novel stochastic variational inference method for Gaussian process () regression, by deriving a posterior over a learnable set of coresets: i.e., over…