3 papers
cs.LG2026
Bivariate Causal Discovery Using Rate-Distortion MDL: An Information Dimension Approach
Tiago Brogueira, Mário A. T. Figueiredo
Approaches to bivariate causal discovery based on the minimum description length (MDL) principle approximate the (uncomputable) Kolmogorov complexity of the models in each causal d…
stat.ML2025
LxCIM: a new rank-based binary classifier performance metric invariant to local exchange of classes
Tiago Brogueira, Mário A. T. Figueiredo
Binary classification is one of the oldest, most prevalent, and studied problems in machine learning. However, the metrics used to evaluate model performance have received comparat…
cs.LG2025
Rethinking Bivariate Causal Discovery Through the Lens of Exchangeability
Tiago Brogueira, Mário Figueiredo
Causal discovery methods have traditionally been developed under two different modeling assumptions: independent and identically distributed (i.i.d.) data and time series data. In…