9 papers
Tabular foundation models for the estimation of probabilistic quasar photometric redshifts in S-PLUS
Raquel R. Valença, Lilianne Nakazono, Rafael Izbicki +6
We assess whether tabular foundation models can be used as off-the-shelf probabilistic photometric-redshift estimators for quasars in the 12-band S-PLUS DR6 survey, where colour-re…
On Focusing Statistical Power for Searches and Measurements in Particle Physics
James Carzon, Aishik Ghosh, Rafael Izbicki +3
Particle physics experiments rely on the (generalised) likelihood ratio test (LRT) for searches and measurements, which consist of composite hypothesis tests. However, this test is…
Benchmarking Tabular Foundation Models for Conditional Density Estimation in Regression
Rafael Izbicki, Pedro L. C. Rodrigues
Conditional density estimation (CDE) - recovering the full conditional distribution of a response given tabular covariates - is essential in settings with heteroscedasticity, multi…
LoBoost: Fast Model-Native Local Conformal Prediction for Gradient-Boosted Trees
Vagner Santos, Victor Coscrato, Luben Cabezas +2
Gradient-boosted decision trees are among the strongest off-the-shelf predictors for tabular regression, but point predictions alone do not quantify uncertainty. Conformal predicti…
Trustworthy scientific inference with generative models
James Carzon, Luca Masserano, Joshua D. Ingram +7
Generative artificial intelligence (AI) excels at producing complex data structures (text, images, videos) by learning patterns from training examples. Across scientific discipline…
DiNo and RanBu: Lightweight Predictions from Shallow Random Forests
Tiago Mendonça dos Santos, Rafael Izbicki, LuÃs Gustavo Esteves
Random Forest ensembles are a strong baseline for tabular prediction tasks, but their reliance on hundreds of deep trees often results in high inference latency and memory demands,…