no code implementations • 3 May 2024 • Alvaro H. C. Correia, Fabio Valerio Massoli, Christos Louizos, Arash Behboodi
Conformal Prediction (CP) is a distribution-free uncertainty estimation framework that constructs prediction sets guaranteed to contain the true answer with a user-specified probability.
1 code implementation • 21 Sep 2022 • Alvaro H. C. Correia, Gennaro Gala, Erik Quaeghebeur, Cassio de Campos, Robert Peharz
Meanwhile, tractable probabilistic models such as probabilistic circuits (PCs) can be understood as hierarchical discrete mixture models, and thus are capable of performing exact inference efficiently but often show subpar performance in comparison to continuous latent-space models.
no code implementations • 4 Mar 2022 • Alvaro H. C. Correia, Daniel E. Worrall, Roberto Bondesan
Simulated annealing (SA) is a stochastic global optimisation technique applicable to a wide range of discrete and continuous variable problems.
1 code implementation • 11 Jul 2020 • Alvaro H. C. Correia, Robert Peharz, Cassio de Campos
Decision Trees and Random Forests are among the most widely used machine learning models, and often achieve state-of-the-art performance in tabular, domain-agnostic datasets.
1 code implementation • NeurIPS 2020 • Alvaro H. C. Correia, Robert Peharz, Cassio de Campos
Decision Trees (DTs) and Random Forests (RFs) are powerful discriminative learners and tools of central importance to the everyday machine learning practitioner and data scientist.
1 code implementation • 23 May 2019 • Alvaro H. C. Correia, James Cussens, Cassio de Campos
Many algorithms for score-based Bayesian network structure learning (BNSL), in particular exact ones, take as input a collection of potentially optimal parent sets for each variable in the data.